diff --git a/README.md b/README.md
index 687b73d..0e595f1 100755
--- a/README.md
+++ b/README.md
@@ -12,6 +12,13 @@ Official implementation of
 
 ----
 
+**Announcement: training just got ~4.5x faster!**
+
+Expect **~4.5x** faster wall-time (depending on hardware and task) with the most recent release (Nov 10, 2024). A majority of the speedups in this branch are enabled with the additional flag `compile=true`. To run the code with `compile=true`, **you will need to install recent `nightly` versions of PyTorch, TensorDict, and TorchRL**. See `docker/environment.yaml` for a tested configuration. Thank you to [Vincent Moens](https://github.com/vmoens) who has been a key contributor to our torch.compile compatibility!
+
+----
+
+
 ## Overview
 
 TD-MPC**2** is a scalable, robust model-based reinforcement learning algorithm. It compares favorably to existing model-free and model-based methods across **104** continuous control tasks spanning multiple domains, with a *single* set of hyperparameters (*right*). We further demonstrate the scalability of TD-MPC**2** by training a single 317M parameter agent to perform **80** tasks across multiple domains, embodiments, and action spaces (*left*). 
diff --git a/datasets/download_mt30.sh b/datasets/download_mt30.sh
new file mode 100644
index 0000000..2073bcb
--- /dev/null
+++ b/datasets/download_mt30.sh
@@ -0,0 +1 @@
+for i in {0..3}; do wget https://huggingface.co/datasets/nicklashansen/tdmpc2/resolve/main/mt30/chunk_${i}.pt?download=true && mv chunk_${i}.pt'?download=true' chunk_${i}.pt; done
\ No newline at end of file
diff --git a/datasets/download_mt80.sh b/datasets/download_mt80.sh
new file mode 100644
index 0000000..01a7c46
--- /dev/null
+++ b/datasets/download_mt80.sh
@@ -0,0 +1 @@
+for i in {0..19}; do wget https://huggingface.co/datasets/nicklashansen/tdmpc2/resolve/main/mt80/chunk_${i}.pt?download=true && mv chunk_${i}.pt'?download=true' chunk_${i}.pt; done
\ No newline at end of file
diff --git a/docker/Dockerfile b/docker/Dockerfile
index dc24103..60fe07c 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -16,31 +16,21 @@
 ##########################################
 
 # base image
-FROM nvidia/cudagl:11.3.1-devel-ubuntu20.04
+FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
 ENV DEBIAN_FRONTEND=noninteractive
 
 # packages
 RUN apt-get -y update && \
-    apt-get install -y --no-install-recommends build-essential git nano rsync vim tree curl \
-    wget unzip htop tmux xvfb patchelf ca-certificates bash-completion libjpeg-dev libpng-dev \
-    ffmpeg cmake swig libssl-dev libcurl4-openssl-dev libopenmpi-dev python3-dev zlib1g-dev \
-    qtbase5-dev qtdeclarative5-dev libglib2.0-0 libglu1-mesa-dev libgl1-mesa-dev libvulkan1 \
-    libgl1-mesa-glx libosmesa6 libosmesa6-dev libglew-dev mesa-utils && \
+    apt-get install -y --no-install-recommends build-essential git nano rsync vim tree curl wget \
+    swig ffmpeg unzip htop tmux xvfb ca-certificates bash-completion libjpeg-dev libpng-dev \
+    libssl-dev libcurl4-openssl-dev libopenmpi-dev zlib1g-dev qtbase5-dev qtdeclarative5-dev \
+    libglib2.0-0 libglu1-mesa-dev libgl1-mesa-dev libvulkan1 libgl1-mesa-glx libosmesa6 \
+    libosmesa6-dev libglew-dev mesa-utils && \
     apt-get clean && \
     apt-get autoremove -y && \
     rm -rf /var/lib/apt/lists/* && \
     mkdir /root/.ssh
 
-# miniconda
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
-    /bin/bash ~/miniconda.sh -b -p /opt/conda && \
-    rm ~/miniconda.sh && \
-    . /opt/conda/etc/profile.d/conda.sh && \
-    conda init && \
-    conda clean -ya
-ENV PATH /opt/conda/bin:$PATH
-SHELL ["/bin/bash", "-c"]
-
 # conda environment
 COPY nvidia_icd.json /usr/share/vulkan/icd.d/nvidia_icd.json
 COPY environment.yaml /root
@@ -48,7 +38,10 @@ RUN conda update conda && \
     conda env update -n base -f /root/environment.yaml && \
     rm /root/environment.yaml && \
     conda clean -ya && \
-    pip cache purge
+    pip cache purge && \
+    conda init
+SHELL ["/bin/bash", "-c"]
+RUN echo "cd /root" >> /root/.bashrc
 
 # mujoco 2.1.0
 ENV MUJOCO_GL egl
diff --git a/docker/environment.yaml b/docker/environment.yaml
index 3081b8d..45eeec7 100644
--- a/docker/environment.yaml
+++ b/docker/environment.yaml
@@ -1,55 +1,47 @@
-name: tdmpc2
+name: episodic
 channels:
   - pytorch-nightly
   - nvidia
   - conda-forge
   - defaults
 dependencies:
-  - cudatoolkit=11.7
-  - glew=2.1.0
-  - glib=2.68.4
-  - pip=21.0
-  - python=3.9.0
-  - pytorch>=2.2.2
-  - torchvision>=0.16.2
+  - glew=2.2.0
+  - glib=2.78.4
+  - pip=24.0
+  - python=3.9
+  - pytorch=2.6.0.dev20241112
+  - pytorch-cuda=12.4
+  - torchvision=0.15.2
   - pip:
-    - absl-py==2.0.0
-    - "cython<3"
-    - dm-control==1.0.8
+    - dm-control==1.0.16
+    - glfw==2.7.0
+    - gymnasium==0.29.1
     - ffmpeg==1.4
-    - glfw==2.6.4
+    - imageio==2.34.1
+    - imageio-ffmpeg==0.4.9
+    - h5py==3.11.0
     - hydra-core==1.3.2
     - hydra-submitit-launcher==1.2.0
-    - imageio==2.33.1
-    - imageio-ffmpeg==0.4.9
-    - kornia==0.7.1
-    - moviepy==1.0.3
-    - mujoco==2.3.1
-    - mujoco-py==2.1.2.14
-    - numpy==1.23.5
-    - omegaconf==2.3.0
-    - open3d==0.18.0
-    - opencv-contrib-python==4.9.0.80
-    - opencv-python==4.9.0.80
-    - pandas==2.1.4
-    - sapien==2.2.1
     - submitit==1.5.1
-    - setuptools==65.5.0
-    - patchelf==0.17.2.1
-    - protobuf==4.25.2
-    - pillow==10.2.0
-    - pyquaternion==0.9.9
-    - tensordict-nightly==2024.3.26
+    - omegaconf==2.3.0
+    - moviepy==1.0.3
+    - mujoco==3.1.2
+    - numpy==1.24.4
+    - tensordict-nightly==2025.1.1
+    - torchrl-nightly==2025.1.1
+    - kornia==0.7.2
     - termcolor==2.4.0
-    - torchrl-nightly==2024.3.26
-    - transforms3d==0.4.1
-    - trimesh==4.0.9
-    - tqdm==4.66.1
-    - wandb==0.16.2
-    - wheel==0.38.0
+    - tqdm==4.66.4
+    - pandas==2.0.3
+    - wandb==0.17.4
     ####################
     # Gym:
-    # (unmaintained but required for maniskill2/meta-world/myosuite)
+    # (unmaintained but required for maniskill2/meta-world)
+    # - "cython<3"
+    # - wheel==0.38.0
+    # - setuptools==65.5.0
+    # - mujoco==2.3.1
+    # - mujoco-py==2.1.2.14
     # - gym==0.21.0
     ####################
     # ManiSkill2:
@@ -61,6 +53,5 @@ dependencies:
     # - git+https://github.com/Farama-Foundation/Metaworld.git@04be337a12305e393c0caf0cbf5ec7755c7c8feb
     ####################
     # MyoSuite:
-    # (requires gym==0.13 which conflicts with meta-world / mani-skill2)
     # - myosuite
     ####################
diff --git a/results/dreamerv3/acrobot-swingup.csv b/results/dreamerv3/acrobot-swingup.csv
new file mode 100644
index 0000000..a367baa
--- /dev/null
+++ b/results/dreamerv3/acrobot-swingup.csv
@@ -0,0 +1,120 @@
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diff --git a/results/dreamerv3/cartpole-balance-sparse.csv b/results/dreamerv3/cartpole-balance-sparse.csv
new file mode 100644
index 0000000..8093ddb
--- /dev/null
+++ b/results/dreamerv3/cartpole-balance-sparse.csv
@@ -0,0 +1,119 @@
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diff --git a/results/dreamerv3/cartpole-balance.csv b/results/dreamerv3/cartpole-balance.csv
new file mode 100644
index 0000000..cd33100
--- /dev/null
+++ b/results/dreamerv3/cartpole-balance.csv
@@ -0,0 +1,117 @@
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diff --git a/results/dreamerv3/cartpole-swingup-sparse.csv b/results/dreamerv3/cartpole-swingup-sparse.csv
new file mode 100644
index 0000000..aba0e27
--- /dev/null
+++ b/results/dreamerv3/cartpole-swingup-sparse.csv
@@ -0,0 +1,117 @@
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diff --git a/results/dreamerv3/cheetah-jump.csv b/results/dreamerv3/cheetah-jump.csv
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diff --git a/results/dreamerv3/cheetah-run-back.csv b/results/dreamerv3/cheetah-run-back.csv
new file mode 100644
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new file mode 100644
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diff --git a/results/dreamerv3/cheetah-run-front.csv b/results/dreamerv3/cheetah-run-front.csv
new file mode 100644
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new file mode 100644
index 0000000..4a87656
--- /dev/null
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new file mode 100644
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diff --git a/results/dreamerv3/cup-spin.csv b/results/dreamerv3/cup-spin.csv
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diff --git a/results/dreamerv3/dog-run.csv b/results/dreamerv3/dog-run.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/dreamerv3/dog-stand.csv b/results/dreamerv3/dog-stand.csv
new file mode 100644
index 0000000..3371075
--- /dev/null
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diff --git a/results/dreamerv3/dog-trot.csv b/results/dreamerv3/dog-trot.csv
new file mode 100644
index 0000000..54437e2
--- /dev/null
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diff --git a/results/dreamerv3/finger-spin.csv b/results/dreamerv3/finger-spin.csv
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diff --git a/results/dreamerv3/finger-turn-easy.csv b/results/dreamerv3/finger-turn-easy.csv
new file mode 100644
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diff --git a/results/dreamerv3/finger-turn-hard.csv b/results/dreamerv3/finger-turn-hard.csv
new file mode 100644
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diff --git a/results/dreamerv3/fish-swim.csv b/results/dreamerv3/fish-swim.csv
new file mode 100644
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diff --git a/results/dreamerv3/hopper-hop-backwards.csv b/results/dreamerv3/hopper-hop-backwards.csv
new file mode 100644
index 0000000..f0d7ddc
--- /dev/null
+++ b/results/dreamerv3/hopper-hop-backwards.csv
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diff --git a/results/dreamerv3/hopper-stand.csv b/results/dreamerv3/hopper-stand.csv
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diff --git a/results/dreamerv3/humanoid-run.csv b/results/dreamerv3/humanoid-run.csv
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diff --git a/results/dreamerv3/humanoid-stand.csv b/results/dreamerv3/humanoid-stand.csv
new file mode 100644
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diff --git a/results/dreamerv3/mw-basketball.csv b/results/dreamerv3/mw-basketball.csv
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diff --git a/results/dreamerv3/mw-box-close.csv b/results/dreamerv3/mw-box-close.csv
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diff --git a/results/dreamerv3/mw-button-press-topdown-wall.csv b/results/dreamerv3/mw-button-press-topdown-wall.csv
new file mode 100644
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diff --git a/results/dreamerv3/mw-button-press-topdown.csv b/results/dreamerv3/mw-button-press-topdown.csv
new file mode 100644
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diff --git a/results/dreamerv3/mw-button-press.csv b/results/dreamerv3/mw-button-press.csv
new file mode 100644
index 0000000..b3dab26
--- /dev/null
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diff --git a/results/dreamerv3/mw-coffee-button.csv b/results/dreamerv3/mw-coffee-button.csv
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--- /dev/null
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diff --git a/results/dreamerv3/mw-coffee-pull.csv b/results/dreamerv3/mw-coffee-pull.csv
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--- /dev/null
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diff --git a/results/dreamerv3/mw-coffee-push.csv b/results/dreamerv3/mw-coffee-push.csv
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diff --git a/results/dreamerv3/mw-dial-turn.csv b/results/dreamerv3/mw-dial-turn.csv
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diff --git a/results/dreamerv3/mw-disassemble.csv b/results/dreamerv3/mw-disassemble.csv
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diff --git a/results/dreamerv3/mw-door-close.csv b/results/dreamerv3/mw-door-close.csv
new file mode 100644
index 0000000..aaea3b2
--- /dev/null
+++ b/results/dreamerv3/mw-door-close.csv
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diff --git a/results/dreamerv3/mw-door-lock.csv b/results/dreamerv3/mw-door-lock.csv
new file mode 100644
index 0000000..02a7e1b
--- /dev/null
+++ b/results/dreamerv3/mw-door-lock.csv
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diff --git a/results/dreamerv3/mw-door-open.csv b/results/dreamerv3/mw-door-open.csv
new file mode 100644
index 0000000..608175c
--- /dev/null
+++ b/results/dreamerv3/mw-door-open.csv
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diff --git a/results/dreamerv3/mw-door-unlock.csv b/results/dreamerv3/mw-door-unlock.csv
new file mode 100644
index 0000000..a47155a
--- /dev/null
+++ b/results/dreamerv3/mw-door-unlock.csv
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diff --git a/results/dreamerv3/mw-drawer-close.csv b/results/dreamerv3/mw-drawer-close.csv
new file mode 100644
index 0000000..c1e4cfa
--- /dev/null
+++ b/results/dreamerv3/mw-drawer-close.csv
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diff --git a/results/dreamerv3/mw-faucet-close.csv b/results/dreamerv3/mw-faucet-close.csv
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diff --git a/results/dreamerv3/mw-hammer.csv b/results/dreamerv3/mw-hammer.csv
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diff --git a/results/dreamerv3/mw-hand-insert.csv b/results/dreamerv3/mw-hand-insert.csv
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diff --git a/results/dreamerv3/mw-handle-press-side.csv b/results/dreamerv3/mw-handle-press-side.csv
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--- /dev/null
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diff --git a/results/dreamerv3/mw-handle-press.csv b/results/dreamerv3/mw-handle-press.csv
new file mode 100644
index 0000000..0f5ac41
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diff --git a/results/dreamerv3/mw-handle-pull-side.csv b/results/dreamerv3/mw-handle-pull-side.csv
new file mode 100644
index 0000000..2f833fa
--- /dev/null
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diff --git a/results/dreamerv3/mw-handle-pull.csv b/results/dreamerv3/mw-handle-pull.csv
new file mode 100644
index 0000000..3589cef
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diff --git a/results/dreamerv3/mw-lever-pull.csv b/results/dreamerv3/mw-lever-pull.csv
new file mode 100644
index 0000000..b78c669
--- /dev/null
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diff --git a/results/dreamerv3/mw-peg-insert-side.csv b/results/dreamerv3/mw-peg-insert-side.csv
new file mode 100644
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diff --git a/results/dreamerv3/mw-peg-unplug-side.csv b/results/dreamerv3/mw-peg-unplug-side.csv
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diff --git a/results/dreamerv3/mw-pick-out-of-hole.csv b/results/dreamerv3/mw-pick-out-of-hole.csv
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diff --git a/results/dreamerv3/mw-pick-place-wall.csv b/results/dreamerv3/mw-pick-place-wall.csv
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diff --git a/results/dreamerv3/mw-pick-place.csv b/results/dreamerv3/mw-pick-place.csv
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diff --git a/results/dreamerv3/mw-plate-slide-back-side.csv b/results/dreamerv3/mw-plate-slide-back-side.csv
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diff --git a/results/dreamerv3/mw-plate-slide-back.csv b/results/dreamerv3/mw-plate-slide-back.csv
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--- /dev/null
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diff --git a/results/dreamerv3/mw-plate-slide-side.csv b/results/dreamerv3/mw-plate-slide-side.csv
new file mode 100644
index 0000000..9ae3f3d
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diff --git a/results/dreamerv3/mw-plate-slide.csv b/results/dreamerv3/mw-plate-slide.csv
new file mode 100644
index 0000000..d946eea
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diff --git a/results/dreamerv3/mw-push-back.csv b/results/dreamerv3/mw-push-back.csv
new file mode 100644
index 0000000..ec1fb00
--- /dev/null
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diff --git a/results/dreamerv3/mw-push-wall.csv b/results/dreamerv3/mw-push-wall.csv
new file mode 100644
index 0000000..5f4ae64
--- /dev/null
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diff --git a/results/dreamerv3/mw-shelf-place.csv b/results/dreamerv3/mw-shelf-place.csv
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diff --git a/results/dreamerv3/mw-soccer.csv b/results/dreamerv3/mw-soccer.csv
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diff --git a/results/dreamerv3/mw-stick-pull.csv b/results/dreamerv3/mw-stick-pull.csv
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diff --git a/results/dreamerv3/mw-stick-push.csv b/results/dreamerv3/mw-stick-push.csv
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diff --git a/results/dreamerv3/mw-sweep-into.csv b/results/dreamerv3/mw-sweep-into.csv
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diff --git a/results/dreamerv3/mw-window-close.csv b/results/dreamerv3/mw-window-close.csv
new file mode 100644
index 0000000..e64806b
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diff --git a/results/dreamerv3/mw-window-open.csv b/results/dreamerv3/mw-window-open.csv
new file mode 100644
index 0000000..1961974
--- /dev/null
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diff --git a/results/dreamerv3/myo-hand-key-turn-hard.csv b/results/dreamerv3/myo-hand-key-turn-hard.csv
new file mode 100644
index 0000000..7fab4f8
--- /dev/null
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diff --git a/results/dreamerv3/myo-hand-key-turn.csv b/results/dreamerv3/myo-hand-key-turn.csv
new file mode 100644
index 0000000..1d733b6
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diff --git a/results/dreamerv3/myo-hand-obj-hold-hard.csv b/results/dreamerv3/myo-hand-obj-hold-hard.csv
new file mode 100644
index 0000000..e0a4afd
--- /dev/null
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diff --git a/results/dreamerv3/myo-hand-obj-hold.csv b/results/dreamerv3/myo-hand-obj-hold.csv
new file mode 100644
index 0000000..0597b83
--- /dev/null
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diff --git a/results/dreamerv3/myo-hand-pen-twirl-hard.csv b/results/dreamerv3/myo-hand-pen-twirl-hard.csv
new file mode 100644
index 0000000..1873e6c
--- /dev/null
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diff --git a/results/dreamerv3/myo-hand-pen-twirl.csv b/results/dreamerv3/myo-hand-pen-twirl.csv
new file mode 100644
index 0000000..998c64e
--- /dev/null
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/results/dreamerv3/pendulum-swingup.csv b/results/dreamerv3/pendulum-swingup.csv
new file mode 100644
index 0000000..b2681ef
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diff --git a/results/dreamerv3/pick-cube.csv b/results/dreamerv3/pick-cube.csv
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diff --git a/results/dreamerv3/pick-ycb.csv b/results/dreamerv3/pick-ycb.csv
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diff --git a/results/dreamerv3/quadruped-run.csv b/results/dreamerv3/quadruped-run.csv
new file mode 100644
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diff --git a/results/dreamerv3/quadruped-walk.csv b/results/dreamerv3/quadruped-walk.csv
new file mode 100644
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diff --git a/results/dreamerv3/reacher-easy.csv b/results/dreamerv3/reacher-easy.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/dreamerv3/reacher-hard.csv b/results/dreamerv3/reacher-hard.csv
new file mode 100644
index 0000000..2ba8cb8
--- /dev/null
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diff --git a/results/dreamerv3/reacher-three-easy.csv b/results/dreamerv3/reacher-three-easy.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/dreamerv3/reacher-three-hard.csv b/results/dreamerv3/reacher-three-hard.csv
new file mode 100644
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diff --git a/results/dreamerv3/stack-cube.csv b/results/dreamerv3/stack-cube.csv
new file mode 100644
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diff --git a/results/dreamerv3/turn-faucet.csv b/results/dreamerv3/turn-faucet.csv
new file mode 100644
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diff --git a/results/dreamerv3/walker-run-backwards.csv b/results/dreamerv3/walker-run-backwards.csv
new file mode 100644
index 0000000..391a77a
--- /dev/null
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new file mode 100644
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diff --git a/results/sac/cartpole-swingup-sparse.csv b/results/sac/cartpole-swingup-sparse.csv
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new file mode 100644
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diff --git a/results/sac/cheetah-run-backwards.csv b/results/sac/cheetah-run-backwards.csv
new file mode 100644
index 0000000..ad7612b
--- /dev/null
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diff --git a/results/sac/cheetah-run-front.csv b/results/sac/cheetah-run-front.csv
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--- /dev/null
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diff --git a/results/sac/cheetah-run.csv b/results/sac/cheetah-run.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/sac/cup-catch.csv b/results/sac/cup-catch.csv
new file mode 100644
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--- /dev/null
+++ b/results/sac/cup-catch.csv
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diff --git a/results/sac/cup-spin.csv b/results/sac/cup-spin.csv
new file mode 100644
index 0000000..8b549a1
--- /dev/null
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diff --git a/results/sac/dog-run.csv b/results/sac/dog-run.csv
new file mode 100644
index 0000000..e6c8bbd
--- /dev/null
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diff --git a/results/sac/dog-stand.csv b/results/sac/dog-stand.csv
new file mode 100644
index 0000000..2566241
--- /dev/null
+++ b/results/sac/dog-stand.csv
@@ -0,0 +1,223 @@
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diff --git a/results/sac/dog-trot.csv b/results/sac/dog-trot.csv
new file mode 100644
index 0000000..d821197
--- /dev/null
+++ b/results/sac/dog-trot.csv
@@ -0,0 +1,424 @@
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diff --git a/results/sac/dog-walk.csv b/results/sac/dog-walk.csv
new file mode 100644
index 0000000..a9b4a33
--- /dev/null
+++ b/results/sac/dog-walk.csv
@@ -0,0 +1,424 @@
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diff --git a/results/sac/finger-spin.csv b/results/sac/finger-spin.csv
new file mode 100644
index 0000000..b47e103
--- /dev/null
+++ b/results/sac/finger-spin.csv
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diff --git a/results/sac/finger-turn-easy.csv b/results/sac/finger-turn-easy.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/sac/finger-turn-hard.csv b/results/sac/finger-turn-hard.csv
new file mode 100644
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diff --git a/results/sac/fish-swim.csv b/results/sac/fish-swim.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/sac/hopper-hop-backwards.csv b/results/sac/hopper-hop-backwards.csv
new file mode 100644
index 0000000..3229ca7
--- /dev/null
+++ b/results/sac/hopper-hop-backwards.csv
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new file mode 100644
index 0000000..23ec66a
--- /dev/null
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--- /dev/null
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diff --git a/results/sac/humanoid-run.csv b/results/sac/humanoid-run.csv
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diff --git a/results/sac/humanoid-walk.csv b/results/sac/humanoid-walk.csv
new file mode 100644
index 0000000..3f8a139
--- /dev/null
+++ b/results/sac/humanoid-walk.csv
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diff --git a/results/sac/lift-cube.csv b/results/sac/lift-cube.csv
new file mode 100644
index 0000000..83c8866
--- /dev/null
+++ b/results/sac/lift-cube.csv
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diff --git a/results/sac/mw-box-close.csv b/results/sac/mw-box-close.csv
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diff --git a/results/sac/mw-button-press-topdown-wall.csv b/results/sac/mw-button-press-topdown-wall.csv
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diff --git a/results/sac/mw-coffee-button.csv b/results/sac/mw-coffee-button.csv
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diff --git a/results/sac/mw-coffee-pull.csv b/results/sac/mw-coffee-pull.csv
new file mode 100644
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diff --git a/results/sac/mw-coffee-push.csv b/results/sac/mw-coffee-push.csv
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diff --git a/results/sac/mw-dial-turn.csv b/results/sac/mw-dial-turn.csv
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--- /dev/null
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diff --git a/results/sac/mw-disassemble.csv b/results/sac/mw-disassemble.csv
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index 0000000..7fdacfc
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diff --git a/results/sac/mw-door-close.csv b/results/sac/mw-door-close.csv
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index 0000000..3c34c97
--- /dev/null
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diff --git a/results/sac/mw-door-lock.csv b/results/sac/mw-door-lock.csv
new file mode 100644
index 0000000..b0fed2b
--- /dev/null
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diff --git a/results/sac/mw-door-open.csv b/results/sac/mw-door-open.csv
new file mode 100644
index 0000000..39c0d6e
--- /dev/null
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diff --git a/results/sac/mw-door-unlock.csv b/results/sac/mw-door-unlock.csv
new file mode 100644
index 0000000..a24fbdf
--- /dev/null
+++ b/results/sac/mw-door-unlock.csv
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diff --git a/results/sac/mw-drawer-close.csv b/results/sac/mw-drawer-close.csv
new file mode 100644
index 0000000..28f273c
--- /dev/null
+++ b/results/sac/mw-drawer-close.csv
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diff --git a/results/sac/mw-drawer-open.csv b/results/sac/mw-drawer-open.csv
new file mode 100644
index 0000000..b462382
--- /dev/null
+++ b/results/sac/mw-drawer-open.csv
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diff --git a/results/sac/mw-faucet-close.csv b/results/sac/mw-faucet-close.csv
new file mode 100644
index 0000000..414859b
--- /dev/null
+++ b/results/sac/mw-faucet-close.csv
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diff --git a/results/sac/mw-hammer.csv b/results/sac/mw-hammer.csv
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diff --git a/results/sac/mw-hand-insert.csv b/results/sac/mw-hand-insert.csv
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diff --git a/results/sac/mw-handle-press-side.csv b/results/sac/mw-handle-press-side.csv
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diff --git a/results/sac/mw-handle-press.csv b/results/sac/mw-handle-press.csv
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diff --git a/results/sac/mw-handle-pull-side.csv b/results/sac/mw-handle-pull-side.csv
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diff --git a/results/sac/mw-handle-pull.csv b/results/sac/mw-handle-pull.csv
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index 0000000..0db9e60
--- /dev/null
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diff --git a/results/sac/mw-lever-pull.csv b/results/sac/mw-lever-pull.csv
new file mode 100644
index 0000000..143376e
--- /dev/null
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diff --git a/results/sac/mw-peg-insert-side.csv b/results/sac/mw-peg-insert-side.csv
new file mode 100644
index 0000000..864789c
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diff --git a/results/sac/mw-peg-unplug-side.csv b/results/sac/mw-peg-unplug-side.csv
new file mode 100644
index 0000000..ecdde7f
--- /dev/null
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diff --git a/results/sac/mw-plate-slide-back-side.csv b/results/sac/mw-plate-slide-back-side.csv
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diff --git a/results/sac/mw-plate-slide-back.csv b/results/sac/mw-plate-slide-back.csv
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diff --git a/results/sac/mw-plate-slide-side.csv b/results/sac/mw-plate-slide-side.csv
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diff --git a/results/sac/mw-push-back.csv b/results/sac/mw-push-back.csv
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diff --git a/results/sac/mw-push-wall.csv b/results/sac/mw-push-wall.csv
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diff --git a/results/sac/mw-push.csv b/results/sac/mw-push.csv
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diff --git a/results/sac/mw-reach-wall.csv b/results/sac/mw-reach-wall.csv
new file mode 100644
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diff --git a/results/sac/mw-reach.csv b/results/sac/mw-reach.csv
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index 0000000..d5ca905
--- /dev/null
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diff --git a/results/sac/mw-shelf-place.csv b/results/sac/mw-shelf-place.csv
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diff --git a/results/sac/mw-soccer.csv b/results/sac/mw-soccer.csv
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diff --git a/results/sac/mw-stick-pull.csv b/results/sac/mw-stick-pull.csv
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diff --git a/results/sac/mw-stick-push.csv b/results/sac/mw-stick-push.csv
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diff --git a/results/sac/mw-sweep-into.csv b/results/sac/mw-sweep-into.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/sac/mw-sweep.csv b/results/sac/mw-sweep.csv
new file mode 100644
index 0000000..cd9b375
--- /dev/null
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diff --git a/results/sac/mw-window-close.csv b/results/sac/mw-window-close.csv
new file mode 100644
index 0000000..51e3a83
--- /dev/null
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diff --git a/results/sac/mw-window-open.csv b/results/sac/mw-window-open.csv
new file mode 100644
index 0000000..eee2e4b
--- /dev/null
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diff --git a/results/sac/myo-hand-key-turn-hard.csv b/results/sac/myo-hand-key-turn-hard.csv
new file mode 100644
index 0000000..7a1577a
--- /dev/null
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diff --git a/results/sac/myo-hand-key-turn.csv b/results/sac/myo-hand-key-turn.csv
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index 0000000..6fcd33f
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new file mode 100644
index 0000000..3370d50
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diff --git a/results/sac/myo-hand-pen-twirl-hard.csv b/results/sac/myo-hand-pen-twirl-hard.csv
new file mode 100644
index 0000000..7dc01f1
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diff --git a/results/sac/myo-hand-pen-twirl.csv b/results/sac/myo-hand-pen-twirl.csv
new file mode 100644
index 0000000..bb076c4
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new file mode 100644
index 0000000..7a1577a
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new file mode 100644
index 0000000..3dabffc
--- /dev/null
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diff --git a/results/sac/myo-hand-reach-hard.csv b/results/sac/myo-hand-reach-hard.csv
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new file mode 100644
index 0000000..336fbbe
--- /dev/null
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diff --git a/results/sac/pendulum-spin.csv b/results/sac/pendulum-spin.csv
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new file mode 100644
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diff --git a/results/sac/pick-cube.csv b/results/sac/pick-cube.csv
new file mode 100644
index 0000000..c7cdbc8
--- /dev/null
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diff --git a/results/sac/pick-ycb.csv b/results/sac/pick-ycb.csv
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diff --git a/results/sac/quadruped-run.csv b/results/sac/quadruped-run.csv
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new file mode 100644
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diff --git a/results/sac/reacher-hard.csv b/results/sac/reacher-hard.csv
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--- /dev/null
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diff --git a/results/sac/reacher-three-easy.csv b/results/sac/reacher-three-easy.csv
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--- /dev/null
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diff --git a/results/sac/reacher-three-hard.csv b/results/sac/reacher-three-hard.csv
new file mode 100644
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diff --git a/results/sac/stack-cube.csv b/results/sac/stack-cube.csv
new file mode 100644
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diff --git a/results/sac/turn-faucet.csv b/results/sac/turn-faucet.csv
new file mode 100644
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diff --git a/results/sac/walker-run-backwards.csv b/results/sac/walker-run-backwards.csv
new file mode 100644
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--- /dev/null
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diff --git a/results/sac/walker-run.csv b/results/sac/walker-run.csv
new file mode 100644
index 0000000..5990844
--- /dev/null
+++ b/results/sac/walker-run.csv
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--- /dev/null
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diff --git a/results/sac/walker-walk-backwards.csv b/results/sac/walker-walk-backwards.csv
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--- /dev/null
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new file mode 100644
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new file mode 100644
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diff --git a/results/tdmpc/cartpole-swingup.csv b/results/tdmpc/cartpole-swingup.csv
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diff --git a/results/tdmpc/cheetah-jump.csv b/results/tdmpc/cheetah-jump.csv
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new file mode 100644
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diff --git a/results/tdmpc/cheetah-run-backwards.csv b/results/tdmpc/cheetah-run-backwards.csv
new file mode 100644
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diff --git a/results/tdmpc/cheetah-run-front.csv b/results/tdmpc/cheetah-run-front.csv
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diff --git a/results/tdmpc/cheetah-run.csv b/results/tdmpc/cheetah-run.csv
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diff --git a/results/tdmpc/cup-catch.csv b/results/tdmpc/cup-catch.csv
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diff --git a/results/tdmpc/cup-spin.csv b/results/tdmpc/cup-spin.csv
new file mode 100644
index 0000000..b4b5d2e
--- /dev/null
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diff --git a/results/tdmpc/dog-run.csv b/results/tdmpc/dog-run.csv
new file mode 100644
index 0000000..40e770b
--- /dev/null
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diff --git a/results/tdmpc/dog-stand.csv b/results/tdmpc/dog-stand.csv
new file mode 100644
index 0000000..74609ec
--- /dev/null
+++ b/results/tdmpc/dog-stand.csv
@@ -0,0 +1,506 @@
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diff --git a/results/tdmpc/dog-trot.csv b/results/tdmpc/dog-trot.csv
new file mode 100644
index 0000000..619b123
--- /dev/null
+++ b/results/tdmpc/dog-trot.csv
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new file mode 100644
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diff --git a/results/tdmpc/finger-turn-easy.csv b/results/tdmpc/finger-turn-easy.csv
new file mode 100644
index 0000000..1c58378
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diff --git a/results/tdmpc/finger-turn-hard.csv b/results/tdmpc/finger-turn-hard.csv
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diff --git a/results/tdmpc/fish-swim.csv b/results/tdmpc/fish-swim.csv
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diff --git a/results/tdmpc/hopper-hop-backwards.csv b/results/tdmpc/hopper-hop-backwards.csv
new file mode 100644
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diff --git a/results/tdmpc/hopper-hop.csv b/results/tdmpc/hopper-hop.csv
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diff --git a/results/tdmpc/hopper-stand.csv b/results/tdmpc/hopper-stand.csv
new file mode 100644
index 0000000..4589ee2
--- /dev/null
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diff --git a/results/tdmpc/humanoid-run.csv b/results/tdmpc/humanoid-run.csv
new file mode 100644
index 0000000..3230e7e
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diff --git a/results/tdmpc/humanoid-walk.csv b/results/tdmpc/humanoid-walk.csv
new file mode 100644
index 0000000..63f57d5
--- /dev/null
+++ b/results/tdmpc/humanoid-walk.csv
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diff --git a/results/tdmpc/mw-basketball.csv b/results/tdmpc/mw-basketball.csv
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diff --git a/results/tdmpc/mw-box-close.csv b/results/tdmpc/mw-box-close.csv
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diff --git a/results/tdmpc/mw-button-press-topdown-wall.csv b/results/tdmpc/mw-button-press-topdown-wall.csv
new file mode 100644
index 0000000..a4a2582
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new file mode 100644
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diff --git a/results/tdmpc/mw-button-press.csv b/results/tdmpc/mw-button-press.csv
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diff --git a/results/tdmpc/mw-coffee-button.csv b/results/tdmpc/mw-coffee-button.csv
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diff --git a/results/tdmpc/mw-coffee-pull.csv b/results/tdmpc/mw-coffee-pull.csv
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diff --git a/results/tdmpc/mw-coffee-push.csv b/results/tdmpc/mw-coffee-push.csv
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diff --git a/results/tdmpc/mw-dial-turn.csv b/results/tdmpc/mw-dial-turn.csv
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diff --git a/results/tdmpc/mw-disassemble.csv b/results/tdmpc/mw-disassemble.csv
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diff --git a/results/tdmpc/mw-door-close.csv b/results/tdmpc/mw-door-close.csv
new file mode 100644
index 0000000..9477761
--- /dev/null
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diff --git a/results/tdmpc/mw-door-lock.csv b/results/tdmpc/mw-door-lock.csv
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index 0000000..f487074
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diff --git a/results/tdmpc/mw-door-open.csv b/results/tdmpc/mw-door-open.csv
new file mode 100644
index 0000000..975fee5
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diff --git a/results/tdmpc/mw-door-unlock.csv b/results/tdmpc/mw-door-unlock.csv
new file mode 100644
index 0000000..66cf516
--- /dev/null
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diff --git a/results/tdmpc/mw-drawer-close.csv b/results/tdmpc/mw-drawer-close.csv
new file mode 100644
index 0000000..1f598e6
--- /dev/null
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diff --git a/results/tdmpc/mw-hammer.csv b/results/tdmpc/mw-hammer.csv
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diff --git a/results/tdmpc/mw-hand-insert.csv b/results/tdmpc/mw-hand-insert.csv
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diff --git a/results/tdmpc/mw-handle-press-side.csv b/results/tdmpc/mw-handle-press-side.csv
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diff --git a/results/tdmpc/mw-handle-press.csv b/results/tdmpc/mw-handle-press.csv
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diff --git a/results/tdmpc/mw-handle-pull.csv b/results/tdmpc/mw-handle-pull.csv
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diff --git a/results/tdmpc/mw-lever-pull.csv b/results/tdmpc/mw-lever-pull.csv
new file mode 100644
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diff --git a/results/tdmpc/mw-pick-place-wall.csv b/results/tdmpc/mw-pick-place-wall.csv
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diff --git a/results/tdmpc/mw-plate-slide-back-side.csv b/results/tdmpc/mw-plate-slide-back-side.csv
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diff --git a/results/tdmpc/mw-plate-slide-back.csv b/results/tdmpc/mw-plate-slide-back.csv
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diff --git a/results/tdmpc/mw-plate-slide.csv b/results/tdmpc/mw-plate-slide.csv
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diff --git a/results/tdmpc/mw-push-back.csv b/results/tdmpc/mw-push-back.csv
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diff --git a/results/tdmpc/mw-push-wall.csv b/results/tdmpc/mw-push-wall.csv
new file mode 100644
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diff --git a/results/tdmpc/mw-push.csv b/results/tdmpc/mw-push.csv
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diff --git a/results/tdmpc/mw-reach-wall.csv b/results/tdmpc/mw-reach-wall.csv
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diff --git a/results/tdmpc/mw-reach.csv b/results/tdmpc/mw-reach.csv
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diff --git a/results/tdmpc/mw-shelf-place.csv b/results/tdmpc/mw-shelf-place.csv
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diff --git a/results/tdmpc/mw-soccer.csv b/results/tdmpc/mw-soccer.csv
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diff --git a/results/tdmpc/mw-stick-pull.csv b/results/tdmpc/mw-stick-pull.csv
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diff --git a/results/tdmpc/mw-stick-push.csv b/results/tdmpc/mw-stick-push.csv
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diff --git a/results/tdmpc/mw-sweep-into.csv b/results/tdmpc/mw-sweep-into.csv
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diff --git a/results/tdmpc/mw-sweep.csv b/results/tdmpc/mw-sweep.csv
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diff --git a/results/tdmpc/mw-window-close.csv b/results/tdmpc/mw-window-close.csv
new file mode 100644
index 0000000..88c3780
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diff --git a/results/tdmpc/myo-hand-obj-hold-hard.csv b/results/tdmpc/myo-hand-obj-hold-hard.csv
new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/results/tdmpc/myo-hand-pose-hard.csv b/results/tdmpc/myo-hand-pose-hard.csv
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new file mode 100644
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new file mode 100644
index 0000000..27acd6f
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diff --git a/results/tdmpc/pendulum-spin.csv b/results/tdmpc/pendulum-spin.csv
new file mode 100644
index 0000000..b3b64e5
--- /dev/null
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new file mode 100644
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diff --git a/results/tdmpc/reacher-easy.csv b/results/tdmpc/reacher-easy.csv
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diff --git a/results/tdmpc/reacher-hard.csv b/results/tdmpc/reacher-hard.csv
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diff --git a/results/tdmpc/reacher-three-easy.csv b/results/tdmpc/reacher-three-easy.csv
new file mode 100644
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diff --git a/results/tdmpc/reacher-three-hard.csv b/results/tdmpc/reacher-three-hard.csv
new file mode 100644
index 0000000..76005cd
--- /dev/null
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diff --git a/results/tdmpc/walker-run.csv b/results/tdmpc/walker-run.csv
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new file mode 100644
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diff --git a/results/tdmpc/walker-walk-backwards.csv b/results/tdmpc/walker-walk-backwards.csv
new file mode 100644
index 0000000..68459bf
--- /dev/null
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diff --git a/results/tdmpc2-pixels/acrobot-swingup.csv b/results/tdmpc2-pixels/acrobot-swingup.csv
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new file mode 100644
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new file mode 100644
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diff --git a/results/tdmpc2-pixels/finger-spin.csv b/results/tdmpc2-pixels/finger-spin.csv
new file mode 100644
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diff --git a/results/tdmpc2-pixels/finger-turn-easy.csv b/results/tdmpc2-pixels/finger-turn-easy.csv
new file mode 100644
index 0000000..c49a800
--- /dev/null
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new file mode 100644
index 0000000..dc02075
--- /dev/null
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new file mode 100644
index 0000000..870377a
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diff --git a/results/tdmpc2-pixels/quadruped-walk.csv b/results/tdmpc2-pixels/quadruped-walk.csv
new file mode 100644
index 0000000..1c1062c
--- /dev/null
+++ b/results/tdmpc2-pixels/quadruped-walk.csv
@@ -0,0 +1,64 @@
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diff --git a/results/tdmpc2-pixels/reacher-easy.csv b/results/tdmpc2-pixels/reacher-easy.csv
new file mode 100644
index 0000000..fd5cf65
--- /dev/null
+++ b/results/tdmpc2-pixels/reacher-easy.csv
@@ -0,0 +1,64 @@
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diff --git a/results/tdmpc2-pixels/reacher-hard.csv b/results/tdmpc2-pixels/reacher-hard.csv
new file mode 100644
index 0000000..d062889
--- /dev/null
+++ b/results/tdmpc2-pixels/reacher-hard.csv
@@ -0,0 +1,64 @@
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diff --git a/results/tdmpc2-pixels/walker-run.csv b/results/tdmpc2-pixels/walker-run.csv
new file mode 100644
index 0000000..9add38b
--- /dev/null
+++ b/results/tdmpc2-pixels/walker-run.csv
@@ -0,0 +1,64 @@
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diff --git a/results/tdmpc2-pixels/walker-walk.csv b/results/tdmpc2-pixels/walker-walk.csv
new file mode 100644
index 0000000..8611a2a
--- /dev/null
+++ b/results/tdmpc2-pixels/walker-walk.csv
@@ -0,0 +1,64 @@
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diff --git a/results/acrobot-swingup.csv b/results/tdmpc2/acrobot-swingup.csv
similarity index 100%
rename from results/acrobot-swingup.csv
rename to results/tdmpc2/acrobot-swingup.csv
diff --git a/results/cartpole-balance-sparse.csv b/results/tdmpc2/cartpole-balance-sparse.csv
similarity index 100%
rename from results/cartpole-balance-sparse.csv
rename to results/tdmpc2/cartpole-balance-sparse.csv
diff --git a/results/cartpole-balance.csv b/results/tdmpc2/cartpole-balance.csv
similarity index 100%
rename from results/cartpole-balance.csv
rename to results/tdmpc2/cartpole-balance.csv
diff --git a/results/cartpole-swingup-sparse.csv b/results/tdmpc2/cartpole-swingup-sparse.csv
similarity index 100%
rename from results/cartpole-swingup-sparse.csv
rename to results/tdmpc2/cartpole-swingup-sparse.csv
diff --git a/results/cartpole-swingup.csv b/results/tdmpc2/cartpole-swingup.csv
similarity index 100%
rename from results/cartpole-swingup.csv
rename to results/tdmpc2/cartpole-swingup.csv
diff --git a/results/cheetah-jump.csv b/results/tdmpc2/cheetah-jump.csv
similarity index 100%
rename from results/cheetah-jump.csv
rename to results/tdmpc2/cheetah-jump.csv
diff --git a/results/cheetah-run-back.csv b/results/tdmpc2/cheetah-run-back.csv
similarity index 100%
rename from results/cheetah-run-back.csv
rename to results/tdmpc2/cheetah-run-back.csv
diff --git a/results/cheetah-run-backwards.csv b/results/tdmpc2/cheetah-run-backwards.csv
similarity index 100%
rename from results/cheetah-run-backwards.csv
rename to results/tdmpc2/cheetah-run-backwards.csv
diff --git a/results/cheetah-run-front.csv b/results/tdmpc2/cheetah-run-front.csv
similarity index 100%
rename from results/cheetah-run-front.csv
rename to results/tdmpc2/cheetah-run-front.csv
diff --git a/results/cheetah-run.csv b/results/tdmpc2/cheetah-run.csv
similarity index 100%
rename from results/cheetah-run.csv
rename to results/tdmpc2/cheetah-run.csv
diff --git a/results/cup-catch.csv b/results/tdmpc2/cup-catch.csv
similarity index 100%
rename from results/cup-catch.csv
rename to results/tdmpc2/cup-catch.csv
diff --git a/results/cup-spin.csv b/results/tdmpc2/cup-spin.csv
similarity index 100%
rename from results/cup-spin.csv
rename to results/tdmpc2/cup-spin.csv
diff --git a/results/dog-run.csv b/results/tdmpc2/dog-run.csv
similarity index 100%
rename from results/dog-run.csv
rename to results/tdmpc2/dog-run.csv
diff --git a/results/dog-stand.csv b/results/tdmpc2/dog-stand.csv
similarity index 100%
rename from results/dog-stand.csv
rename to results/tdmpc2/dog-stand.csv
diff --git a/results/dog-trot.csv b/results/tdmpc2/dog-trot.csv
similarity index 100%
rename from results/dog-trot.csv
rename to results/tdmpc2/dog-trot.csv
diff --git a/results/dog-walk.csv b/results/tdmpc2/dog-walk.csv
similarity index 100%
rename from results/dog-walk.csv
rename to results/tdmpc2/dog-walk.csv
diff --git a/results/finger-spin.csv b/results/tdmpc2/finger-spin.csv
similarity index 100%
rename from results/finger-spin.csv
rename to results/tdmpc2/finger-spin.csv
diff --git a/results/finger-turn-easy.csv b/results/tdmpc2/finger-turn-easy.csv
similarity index 100%
rename from results/finger-turn-easy.csv
rename to results/tdmpc2/finger-turn-easy.csv
diff --git a/results/finger-turn-hard.csv b/results/tdmpc2/finger-turn-hard.csv
similarity index 100%
rename from results/finger-turn-hard.csv
rename to results/tdmpc2/finger-turn-hard.csv
diff --git a/results/fish-swim.csv b/results/tdmpc2/fish-swim.csv
similarity index 100%
rename from results/fish-swim.csv
rename to results/tdmpc2/fish-swim.csv
diff --git a/results/hopper-hop-backwards.csv b/results/tdmpc2/hopper-hop-backwards.csv
similarity index 100%
rename from results/hopper-hop-backwards.csv
rename to results/tdmpc2/hopper-hop-backwards.csv
diff --git a/results/hopper-hop.csv b/results/tdmpc2/hopper-hop.csv
similarity index 100%
rename from results/hopper-hop.csv
rename to results/tdmpc2/hopper-hop.csv
diff --git a/results/hopper-stand.csv b/results/tdmpc2/hopper-stand.csv
similarity index 100%
rename from results/hopper-stand.csv
rename to results/tdmpc2/hopper-stand.csv
diff --git a/results/humanoid-run.csv b/results/tdmpc2/humanoid-run.csv
similarity index 100%
rename from results/humanoid-run.csv
rename to results/tdmpc2/humanoid-run.csv
diff --git a/results/humanoid-stand.csv b/results/tdmpc2/humanoid-stand.csv
similarity index 100%
rename from results/humanoid-stand.csv
rename to results/tdmpc2/humanoid-stand.csv
diff --git a/results/humanoid-walk.csv b/results/tdmpc2/humanoid-walk.csv
similarity index 100%
rename from results/humanoid-walk.csv
rename to results/tdmpc2/humanoid-walk.csv
diff --git a/results/lift-cube.csv b/results/tdmpc2/lift-cube.csv
similarity index 100%
rename from results/lift-cube.csv
rename to results/tdmpc2/lift-cube.csv
diff --git a/results/mw-assembly.csv b/results/tdmpc2/mw-assembly.csv
similarity index 100%
rename from results/mw-assembly.csv
rename to results/tdmpc2/mw-assembly.csv
diff --git a/results/mw-basketball.csv b/results/tdmpc2/mw-basketball.csv
similarity index 100%
rename from results/mw-basketball.csv
rename to results/tdmpc2/mw-basketball.csv
diff --git a/results/mw-bin-picking.csv b/results/tdmpc2/mw-bin-picking.csv
similarity index 100%
rename from results/mw-bin-picking.csv
rename to results/tdmpc2/mw-bin-picking.csv
diff --git a/results/mw-box-close.csv b/results/tdmpc2/mw-box-close.csv
similarity index 100%
rename from results/mw-box-close.csv
rename to results/tdmpc2/mw-box-close.csv
diff --git a/results/mw-button-press-topdown-wall.csv b/results/tdmpc2/mw-button-press-topdown-wall.csv
similarity index 100%
rename from results/mw-button-press-topdown-wall.csv
rename to results/tdmpc2/mw-button-press-topdown-wall.csv
diff --git a/results/mw-button-press-topdown.csv b/results/tdmpc2/mw-button-press-topdown.csv
similarity index 100%
rename from results/mw-button-press-topdown.csv
rename to results/tdmpc2/mw-button-press-topdown.csv
diff --git a/results/mw-button-press-wall.csv b/results/tdmpc2/mw-button-press-wall.csv
similarity index 100%
rename from results/mw-button-press-wall.csv
rename to results/tdmpc2/mw-button-press-wall.csv
diff --git a/results/mw-button-press.csv b/results/tdmpc2/mw-button-press.csv
similarity index 100%
rename from results/mw-button-press.csv
rename to results/tdmpc2/mw-button-press.csv
diff --git a/results/mw-coffee-button.csv b/results/tdmpc2/mw-coffee-button.csv
similarity index 100%
rename from results/mw-coffee-button.csv
rename to results/tdmpc2/mw-coffee-button.csv
diff --git a/results/mw-coffee-pull.csv b/results/tdmpc2/mw-coffee-pull.csv
similarity index 100%
rename from results/mw-coffee-pull.csv
rename to results/tdmpc2/mw-coffee-pull.csv
diff --git a/results/mw-coffee-push.csv b/results/tdmpc2/mw-coffee-push.csv
similarity index 100%
rename from results/mw-coffee-push.csv
rename to results/tdmpc2/mw-coffee-push.csv
diff --git a/results/mw-dial-turn.csv b/results/tdmpc2/mw-dial-turn.csv
similarity index 100%
rename from results/mw-dial-turn.csv
rename to results/tdmpc2/mw-dial-turn.csv
diff --git a/results/mw-disassemble.csv b/results/tdmpc2/mw-disassemble.csv
similarity index 100%
rename from results/mw-disassemble.csv
rename to results/tdmpc2/mw-disassemble.csv
diff --git a/results/mw-door-close.csv b/results/tdmpc2/mw-door-close.csv
similarity index 100%
rename from results/mw-door-close.csv
rename to results/tdmpc2/mw-door-close.csv
diff --git a/results/mw-door-lock.csv b/results/tdmpc2/mw-door-lock.csv
similarity index 100%
rename from results/mw-door-lock.csv
rename to results/tdmpc2/mw-door-lock.csv
diff --git a/results/mw-door-open.csv b/results/tdmpc2/mw-door-open.csv
similarity index 100%
rename from results/mw-door-open.csv
rename to results/tdmpc2/mw-door-open.csv
diff --git a/results/mw-door-unlock.csv b/results/tdmpc2/mw-door-unlock.csv
similarity index 100%
rename from results/mw-door-unlock.csv
rename to results/tdmpc2/mw-door-unlock.csv
diff --git a/results/mw-drawer-close.csv b/results/tdmpc2/mw-drawer-close.csv
similarity index 100%
rename from results/mw-drawer-close.csv
rename to results/tdmpc2/mw-drawer-close.csv
diff --git a/results/mw-drawer-open.csv b/results/tdmpc2/mw-drawer-open.csv
similarity index 100%
rename from results/mw-drawer-open.csv
rename to results/tdmpc2/mw-drawer-open.csv
diff --git a/results/mw-faucet-close.csv b/results/tdmpc2/mw-faucet-close.csv
similarity index 100%
rename from results/mw-faucet-close.csv
rename to results/tdmpc2/mw-faucet-close.csv
diff --git a/results/mw-faucet-open.csv b/results/tdmpc2/mw-faucet-open.csv
similarity index 100%
rename from results/mw-faucet-open.csv
rename to results/tdmpc2/mw-faucet-open.csv
diff --git a/results/mw-hammer.csv b/results/tdmpc2/mw-hammer.csv
similarity index 100%
rename from results/mw-hammer.csv
rename to results/tdmpc2/mw-hammer.csv
diff --git a/results/mw-hand-insert.csv b/results/tdmpc2/mw-hand-insert.csv
similarity index 100%
rename from results/mw-hand-insert.csv
rename to results/tdmpc2/mw-hand-insert.csv
diff --git a/results/mw-handle-press-side.csv b/results/tdmpc2/mw-handle-press-side.csv
similarity index 100%
rename from results/mw-handle-press-side.csv
rename to results/tdmpc2/mw-handle-press-side.csv
diff --git a/results/mw-handle-press.csv b/results/tdmpc2/mw-handle-press.csv
similarity index 100%
rename from results/mw-handle-press.csv
rename to results/tdmpc2/mw-handle-press.csv
diff --git a/results/mw-handle-pull-side.csv b/results/tdmpc2/mw-handle-pull-side.csv
similarity index 100%
rename from results/mw-handle-pull-side.csv
rename to results/tdmpc2/mw-handle-pull-side.csv
diff --git a/results/mw-handle-pull.csv b/results/tdmpc2/mw-handle-pull.csv
similarity index 100%
rename from results/mw-handle-pull.csv
rename to results/tdmpc2/mw-handle-pull.csv
diff --git a/results/mw-lever-pull.csv b/results/tdmpc2/mw-lever-pull.csv
similarity index 100%
rename from results/mw-lever-pull.csv
rename to results/tdmpc2/mw-lever-pull.csv
diff --git a/results/mw-peg-insert-side.csv b/results/tdmpc2/mw-peg-insert-side.csv
similarity index 100%
rename from results/mw-peg-insert-side.csv
rename to results/tdmpc2/mw-peg-insert-side.csv
diff --git a/results/mw-peg-unplug-side.csv b/results/tdmpc2/mw-peg-unplug-side.csv
similarity index 100%
rename from results/mw-peg-unplug-side.csv
rename to results/tdmpc2/mw-peg-unplug-side.csv
diff --git a/results/mw-pick-out-of-hole.csv b/results/tdmpc2/mw-pick-out-of-hole.csv
similarity index 100%
rename from results/mw-pick-out-of-hole.csv
rename to results/tdmpc2/mw-pick-out-of-hole.csv
diff --git a/results/mw-pick-place-wall.csv b/results/tdmpc2/mw-pick-place-wall.csv
similarity index 100%
rename from results/mw-pick-place-wall.csv
rename to results/tdmpc2/mw-pick-place-wall.csv
diff --git a/results/mw-pick-place.csv b/results/tdmpc2/mw-pick-place.csv
similarity index 100%
rename from results/mw-pick-place.csv
rename to results/tdmpc2/mw-pick-place.csv
diff --git a/results/mw-plate-slide-back-side.csv b/results/tdmpc2/mw-plate-slide-back-side.csv
similarity index 100%
rename from results/mw-plate-slide-back-side.csv
rename to results/tdmpc2/mw-plate-slide-back-side.csv
diff --git a/results/mw-plate-slide-back.csv b/results/tdmpc2/mw-plate-slide-back.csv
similarity index 100%
rename from results/mw-plate-slide-back.csv
rename to results/tdmpc2/mw-plate-slide-back.csv
diff --git a/results/mw-plate-slide-side.csv b/results/tdmpc2/mw-plate-slide-side.csv
similarity index 100%
rename from results/mw-plate-slide-side.csv
rename to results/tdmpc2/mw-plate-slide-side.csv
diff --git a/results/mw-plate-slide.csv b/results/tdmpc2/mw-plate-slide.csv
similarity index 100%
rename from results/mw-plate-slide.csv
rename to results/tdmpc2/mw-plate-slide.csv
diff --git a/results/mw-push-back.csv b/results/tdmpc2/mw-push-back.csv
similarity index 100%
rename from results/mw-push-back.csv
rename to results/tdmpc2/mw-push-back.csv
diff --git a/results/mw-push-wall.csv b/results/tdmpc2/mw-push-wall.csv
similarity index 100%
rename from results/mw-push-wall.csv
rename to results/tdmpc2/mw-push-wall.csv
diff --git a/results/mw-push.csv b/results/tdmpc2/mw-push.csv
similarity index 100%
rename from results/mw-push.csv
rename to results/tdmpc2/mw-push.csv
diff --git a/results/mw-reach-wall.csv b/results/tdmpc2/mw-reach-wall.csv
similarity index 100%
rename from results/mw-reach-wall.csv
rename to results/tdmpc2/mw-reach-wall.csv
diff --git a/results/mw-reach.csv b/results/tdmpc2/mw-reach.csv
similarity index 100%
rename from results/mw-reach.csv
rename to results/tdmpc2/mw-reach.csv
diff --git a/results/mw-shelf-place.csv b/results/tdmpc2/mw-shelf-place.csv
similarity index 100%
rename from results/mw-shelf-place.csv
rename to results/tdmpc2/mw-shelf-place.csv
diff --git a/results/mw-soccer.csv b/results/tdmpc2/mw-soccer.csv
similarity index 100%
rename from results/mw-soccer.csv
rename to results/tdmpc2/mw-soccer.csv
diff --git a/results/mw-stick-pull.csv b/results/tdmpc2/mw-stick-pull.csv
similarity index 100%
rename from results/mw-stick-pull.csv
rename to results/tdmpc2/mw-stick-pull.csv
diff --git a/results/mw-stick-push.csv b/results/tdmpc2/mw-stick-push.csv
similarity index 100%
rename from results/mw-stick-push.csv
rename to results/tdmpc2/mw-stick-push.csv
diff --git a/results/mw-sweep-into.csv b/results/tdmpc2/mw-sweep-into.csv
similarity index 100%
rename from results/mw-sweep-into.csv
rename to results/tdmpc2/mw-sweep-into.csv
diff --git a/results/mw-sweep.csv b/results/tdmpc2/mw-sweep.csv
similarity index 100%
rename from results/mw-sweep.csv
rename to results/tdmpc2/mw-sweep.csv
diff --git a/results/mw-window-close.csv b/results/tdmpc2/mw-window-close.csv
similarity index 100%
rename from results/mw-window-close.csv
rename to results/tdmpc2/mw-window-close.csv
diff --git a/results/mw-window-open.csv b/results/tdmpc2/mw-window-open.csv
similarity index 100%
rename from results/mw-window-open.csv
rename to results/tdmpc2/mw-window-open.csv
diff --git a/results/myo-hand-key-turn-hard.csv b/results/tdmpc2/myo-hand-key-turn-hard.csv
similarity index 100%
rename from results/myo-hand-key-turn-hard.csv
rename to results/tdmpc2/myo-hand-key-turn-hard.csv
diff --git a/results/myo-hand-key-turn.csv b/results/tdmpc2/myo-hand-key-turn.csv
similarity index 100%
rename from results/myo-hand-key-turn.csv
rename to results/tdmpc2/myo-hand-key-turn.csv
diff --git a/results/myo-hand-obj-hold-hard.csv b/results/tdmpc2/myo-hand-obj-hold-hard.csv
similarity index 100%
rename from results/myo-hand-obj-hold-hard.csv
rename to results/tdmpc2/myo-hand-obj-hold-hard.csv
diff --git a/results/myo-hand-obj-hold.csv b/results/tdmpc2/myo-hand-obj-hold.csv
similarity index 100%
rename from results/myo-hand-obj-hold.csv
rename to results/tdmpc2/myo-hand-obj-hold.csv
diff --git a/results/myo-hand-pen-twirl-hard.csv b/results/tdmpc2/myo-hand-pen-twirl-hard.csv
similarity index 100%
rename from results/myo-hand-pen-twirl-hard.csv
rename to results/tdmpc2/myo-hand-pen-twirl-hard.csv
diff --git a/results/myo-hand-pen-twirl.csv b/results/tdmpc2/myo-hand-pen-twirl.csv
similarity index 100%
rename from results/myo-hand-pen-twirl.csv
rename to results/tdmpc2/myo-hand-pen-twirl.csv
diff --git a/results/myo-hand-pose-hard.csv b/results/tdmpc2/myo-hand-pose-hard.csv
similarity index 100%
rename from results/myo-hand-pose-hard.csv
rename to results/tdmpc2/myo-hand-pose-hard.csv
diff --git a/results/myo-hand-pose.csv b/results/tdmpc2/myo-hand-pose.csv
similarity index 100%
rename from results/myo-hand-pose.csv
rename to results/tdmpc2/myo-hand-pose.csv
diff --git a/results/myo-hand-reach-hard.csv b/results/tdmpc2/myo-hand-reach-hard.csv
similarity index 100%
rename from results/myo-hand-reach-hard.csv
rename to results/tdmpc2/myo-hand-reach-hard.csv
diff --git a/results/myo-hand-reach.csv b/results/tdmpc2/myo-hand-reach.csv
similarity index 100%
rename from results/myo-hand-reach.csv
rename to results/tdmpc2/myo-hand-reach.csv
diff --git a/results/pendulum-spin.csv b/results/tdmpc2/pendulum-spin.csv
similarity index 100%
rename from results/pendulum-spin.csv
rename to results/tdmpc2/pendulum-spin.csv
diff --git a/results/pendulum-swingup.csv b/results/tdmpc2/pendulum-swingup.csv
similarity index 100%
rename from results/pendulum-swingup.csv
rename to results/tdmpc2/pendulum-swingup.csv
diff --git a/results/pick-cube.csv b/results/tdmpc2/pick-cube.csv
similarity index 100%
rename from results/pick-cube.csv
rename to results/tdmpc2/pick-cube.csv
diff --git a/results/pick-ycb.csv b/results/tdmpc2/pick-ycb.csv
similarity index 100%
rename from results/pick-ycb.csv
rename to results/tdmpc2/pick-ycb.csv
diff --git a/results/quadruped-run.csv b/results/tdmpc2/quadruped-run.csv
similarity index 100%
rename from results/quadruped-run.csv
rename to results/tdmpc2/quadruped-run.csv
diff --git a/results/quadruped-walk.csv b/results/tdmpc2/quadruped-walk.csv
similarity index 100%
rename from results/quadruped-walk.csv
rename to results/tdmpc2/quadruped-walk.csv
diff --git a/results/reacher-easy.csv b/results/tdmpc2/reacher-easy.csv
similarity index 100%
rename from results/reacher-easy.csv
rename to results/tdmpc2/reacher-easy.csv
diff --git a/results/reacher-hard.csv b/results/tdmpc2/reacher-hard.csv
similarity index 100%
rename from results/reacher-hard.csv
rename to results/tdmpc2/reacher-hard.csv
diff --git a/results/reacher-three-easy.csv b/results/tdmpc2/reacher-three-easy.csv
similarity index 100%
rename from results/reacher-three-easy.csv
rename to results/tdmpc2/reacher-three-easy.csv
diff --git a/results/reacher-three-hard.csv b/results/tdmpc2/reacher-three-hard.csv
similarity index 100%
rename from results/reacher-three-hard.csv
rename to results/tdmpc2/reacher-three-hard.csv
diff --git a/results/stack-cube.csv b/results/tdmpc2/stack-cube.csv
similarity index 100%
rename from results/stack-cube.csv
rename to results/tdmpc2/stack-cube.csv
diff --git a/results/turn-faucet.csv b/results/tdmpc2/turn-faucet.csv
similarity index 100%
rename from results/turn-faucet.csv
rename to results/tdmpc2/turn-faucet.csv
diff --git a/results/walker-run-backwards.csv b/results/tdmpc2/walker-run-backwards.csv
similarity index 100%
rename from results/walker-run-backwards.csv
rename to results/tdmpc2/walker-run-backwards.csv
diff --git a/results/walker-run.csv b/results/tdmpc2/walker-run.csv
similarity index 100%
rename from results/walker-run.csv
rename to results/tdmpc2/walker-run.csv
diff --git a/results/walker-stand.csv b/results/tdmpc2/walker-stand.csv
similarity index 100%
rename from results/walker-stand.csv
rename to results/tdmpc2/walker-stand.csv
diff --git a/results/walker-walk-backwards.csv b/results/tdmpc2/walker-walk-backwards.csv
similarity index 100%
rename from results/walker-walk-backwards.csv
rename to results/tdmpc2/walker-walk-backwards.csv
diff --git a/results/walker-walk.csv b/results/tdmpc2/walker-walk.csv
similarity index 100%
rename from results/walker-walk.csv
rename to results/tdmpc2/walker-walk.csv
diff --git a/tdmpc2/common/buffer.py b/tdmpc2/common/buffer.py
index f5deaaf..9b74c70 100644
--- a/tdmpc2/common/buffer.py
+++ b/tdmpc2/common/buffer.py
@@ -12,7 +12,7 @@ class Buffer():
 
 	def __init__(self, cfg):
 		self.cfg = cfg
-		self._device = torch.device('cuda')
+		self._device = torch.device('cuda:0')
 		self._capacity = min(cfg.buffer_size, cfg.steps)
 		self._sampler = SliceSampler(
 			num_slices=self.cfg.batch_size,
@@ -20,6 +20,7 @@ class Buffer():
 			traj_key='episode',
 			truncated_key=None,
 			strict_length=True,
+			cache_values=cfg.multitask,
 		)
 		self._batch_size = cfg.batch_size * (cfg.horizon+1)
 		self._num_eps = 0
@@ -28,7 +29,7 @@ class Buffer():
 	def capacity(self):
 		"""Return the capacity of the buffer."""
 		return self._capacity
-	
+
 	@property
 	def num_eps(self):
 		"""Return the number of episodes in the buffer."""
@@ -41,8 +42,8 @@ class Buffer():
 		return ReplayBuffer(
 			storage=storage,
 			sampler=self._sampler,
-			pin_memory=True,
-			prefetch=1,
+			pin_memory=False,
+			prefetch=0,
 			batch_size=self._batch_size,
 		)
 
@@ -58,39 +59,52 @@ class Buffer():
 		total_bytes = bytes_per_step*self._capacity
 		print(f'Storage required: {total_bytes/1e9:.2f} GB')
 		# Heuristic: decide whether to use CUDA or CPU memory
-		storage_device = 'cuda' if 2.5*total_bytes < mem_free else 'cpu'
+		storage_device = 'cuda:0' if 2.5*total_bytes < mem_free else 'cpu'
 		print(f'Using {storage_device.upper()} memory for storage.')
+		self._storage_device = torch.device(storage_device)
 		return self._reserve_buffer(
-			LazyTensorStorage(self._capacity, device=torch.device(storage_device))
+			LazyTensorStorage(self._capacity, device=self._storage_device)
 		)
 
-	def _to_device(self, *args, device=None):
-		if device is None:
-			device = self._device
-		return (arg.to(device, non_blocking=True) \
-			if arg is not None else None for arg in args)
-
-	def _prepare_batch(self, td):
+	def load(self, td):
 		"""
-		Prepare a sampled batch for training (post-processing).
-		Expects `td` to be a TensorDict with batch size TxB.
+		Load a batch of episodes into the buffer. This is useful for loading data from disk,
+		and is more efficient than adding episodes one by one.
 		"""
-		obs = td['obs']
-		action = td['action'][1:]
-		reward = td['reward'][1:].unsqueeze(-1)
-		terminated = td['terminated'][-1].unsqueeze(-1)
-		task = td['task'][0] if 'task' in td.keys() else None
-		return self._to_device(obs, action, reward, terminated, task)
+		num_new_eps = len(td)
+		episode_idx = torch.arange(self._num_eps, self._num_eps+num_new_eps, dtype=torch.int64)
+		td['episode'] = episode_idx.unsqueeze(-1).expand(-1, td['reward'].shape[1])
+		if self._num_eps == 0:
+			self._buffer = self._init(td[0])
+		td = td.reshape(td.shape[0]*td.shape[1])
+		self._buffer.extend(td)
+		self._num_eps += num_new_eps
+		return self._num_eps
 
 	def add(self, td):
 		"""Add an episode to the buffer."""
-		td['episode'] = torch.ones_like(td['reward'], dtype=torch.int64) * self._num_eps
+		td['episode'] = torch.full_like(td['reward'], self._num_eps, dtype=torch.int64)
 		if self._num_eps == 0:
 			self._buffer = self._init(td)
 		self._buffer.extend(td)
 		self._num_eps += 1
 		return self._num_eps
 
+	def _prepare_batch(self, td):
+		"""
+		Prepare a sampled batch for training (post-processing).
+		Expects `td` to be a TensorDict with batch size TxB.
+		"""
+		td = td.select("obs", "action", "reward", "terminated", "task", strict=False).to(self._device, non_blocking=True)
+		obs = td.get('obs').contiguous()
+		action = td.get('action')[1:].contiguous()
+		reward = td.get('reward')[1:].unsqueeze(-1).contiguous()
+		terminated = td.get('terminated')[1:].unsqueeze(-1).contiguous()
+		task = td.get('task', None)
+		if task is not None:
+			task = task[0].contiguous()
+		return obs, action, reward, terminated, task
+
 	def sample(self):
 		"""Sample a batch of subsequences from the buffer."""
 		td = self._buffer.sample().view(-1, self.cfg.horizon+1).permute(1, 0)
diff --git a/tdmpc2/common/layers.py b/tdmpc2/common/layers.py
index 1e0adb3..951d67a 100644
--- a/tdmpc2/common/layers.py
+++ b/tdmpc2/common/layers.py
@@ -1,7 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functorch import combine_state_for_ensemble
+from tensordict import from_modules
+from copy import deepcopy
 
 
 class Ensemble(nn.Module):
@@ -11,17 +12,25 @@ class Ensemble(nn.Module):
 
 	def __init__(self, modules, **kwargs):
 		super().__init__()
-		modules = nn.ModuleList(modules)
-		fn, params, _ = combine_state_for_ensemble(modules)
-		self.vmap = torch.vmap(fn, in_dims=(0, 0, None), randomness='different', **kwargs)
-		self.params = nn.ParameterList([nn.Parameter(p) for p in params])
-		self._repr = str(modules)
+		# combine_state_for_ensemble causes graph breaks
+		self.params = from_modules(*modules, as_module=True)
+		with self.params[0].data.to("meta").to_module(modules[0]):
+			self.module = deepcopy(modules[0])
+		self._repr = str(modules[0])
+		self._n = len(modules)
+
+	def __len__(self):
+		return self._n
+
+	def _call(self, params, *args, **kwargs):
+		with params.to_module(self.module):
+			return self.module(*args, **kwargs)
 
 	def forward(self, *args, **kwargs):
-		return self.vmap([p for p in self.params], (), *args, **kwargs)
+		return torch.vmap(self._call, (0, None), randomness="different")(self.params, *args, **kwargs)
 
 	def __repr__(self):
-		return 'Vectorized ' + self._repr
+		return f'Vectorized {len(self)}x ' + self._repr
 
 
 class ShiftAug(nn.Module):
@@ -32,13 +41,13 @@ class ShiftAug(nn.Module):
 	def __init__(self, pad=3):
 		super().__init__()
 		self.pad = pad
+		self.padding = tuple([self.pad] * 4)
 
 	def forward(self, x):
 		x = x.float()
 		n, _, h, w = x.size()
 		assert h == w
-		padding = tuple([self.pad] * 4)
-		x = F.pad(x, padding, 'replicate')
+		x = F.pad(x, self.padding, 'replicate')
 		eps = 1.0 / (h + 2 * self.pad)
 		arange = torch.linspace(-1.0 + eps, 1.0 - eps, h + 2 * self.pad, device=x.device, dtype=x.dtype)[:h]
 		arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
@@ -59,7 +68,7 @@ class PixelPreprocess(nn.Module):
 		super().__init__()
 
 	def forward(self, x):
-		return x.div_(255.).sub_(0.5)
+		return x.div(255.).sub(0.5)
 
 
 class SimNorm(nn.Module):
@@ -67,17 +76,17 @@ class SimNorm(nn.Module):
 	Simplicial normalization.
 	Adapted from https://arxiv.org/abs/2204.00616.
 	"""
-	
+
 	def __init__(self, cfg):
 		super().__init__()
 		self.dim = cfg.simnorm_dim
-	
+
 	def forward(self, x):
 		shp = x.shape
 		x = x.view(*shp[:-1], -1, self.dim)
 		x = F.softmax(x, dim=-1)
 		return x.view(*shp)
-		
+
 	def __repr__(self):
 		return f"SimNorm(dim={self.dim})"
 
@@ -87,18 +96,20 @@ class NormedLinear(nn.Linear):
 	Linear layer with LayerNorm, activation, and optionally dropout.
 	"""
 
-	def __init__(self, *args, dropout=0., act=nn.Mish(inplace=True), **kwargs):
+	def __init__(self, *args, dropout=0., act=None, **kwargs):
 		super().__init__(*args, **kwargs)
 		self.ln = nn.LayerNorm(self.out_features)
+		if act is None:
+			act = nn.Mish(inplace=False)
 		self.act = act
-		self.dropout = nn.Dropout(dropout, inplace=True) if dropout else None
+		self.dropout = nn.Dropout(dropout, inplace=False) if dropout else None
 
 	def forward(self, x):
 		x = super().forward(x)
 		if self.dropout:
 			x = self.dropout(x)
 		return self.act(self.ln(x))
-	
+
 	def __repr__(self):
 		repr_dropout = f", dropout={self.dropout.p}" if self.dropout else ""
 		return f"NormedLinear(in_features={self.in_features}, "\
@@ -130,9 +141,9 @@ def conv(in_shape, num_channels, act=None):
 	assert in_shape[-1] == 64 # assumes rgb observations to be 64x64
 	layers = [
 		ShiftAug(), PixelPreprocess(),
-		nn.Conv2d(in_shape[0], num_channels, 7, stride=2), nn.ReLU(inplace=True),
-		nn.Conv2d(num_channels, num_channels, 5, stride=2), nn.ReLU(inplace=True),
-		nn.Conv2d(num_channels, num_channels, 3, stride=2), nn.ReLU(inplace=True),
+		nn.Conv2d(in_shape[0], num_channels, 7, stride=2), nn.ReLU(inplace=False),
+		nn.Conv2d(num_channels, num_channels, 5, stride=2), nn.ReLU(inplace=False),
+		nn.Conv2d(num_channels, num_channels, 3, stride=2), nn.ReLU(inplace=False),
 		nn.Conv2d(num_channels, num_channels, 3, stride=1), nn.Flatten()]
 	if act:
 		layers.append(act)
@@ -151,3 +162,59 @@ def enc(cfg, out={}):
 		else:
 			raise NotImplementedError(f"Encoder for observation type {k} not implemented.")
 	return nn.ModuleDict(out)
+
+
+def api_model_conversion(target_state_dict, source_state_dict):
+	"""
+	Converts a checkpoint from our old API to the new torch.compile compatible API.
+	"""
+	# check whether checkpoint is already in the new format
+	if "_detach_Qs_params.0.weight" in source_state_dict:
+		return source_state_dict
+
+	name_map = ['weight', 'bias', 'ln.weight', 'ln.bias']
+	new_state_dict = dict()
+
+	# rename keys
+	for key, val in list(source_state_dict.items()):
+		if key.startswith('_Qs.'):
+			num = key[len('_Qs.params.'):]
+			new_key = str(int(num) // 4) + "." + name_map[int(num) % 4]
+			new_total_key = "_Qs.params." + new_key
+			del source_state_dict[key]
+			new_state_dict[new_total_key] = val
+			new_total_key = "_detach_Qs_params." + new_key
+			new_state_dict[new_total_key] = val
+		elif key.startswith('_target_Qs.'):
+			num = key[len('_target_Qs.params.'):]
+			new_key = str(int(num) // 4) + "." + name_map[int(num) % 4]
+			new_total_key = "_target_Qs_params." + new_key
+			del source_state_dict[key]
+			new_state_dict[new_total_key] = val
+
+	# add batch_size and device from target_state_dict to new_state_dict
+	for prefix in ('_Qs.', '_detach_Qs_', '_target_Qs_'):
+		for key in ('__batch_size', '__device'):
+			new_key = prefix + 'params.' + key
+			new_state_dict[new_key] = target_state_dict[new_key]
+
+	# check that every key in new_state_dict is in target_state_dict
+	for key in new_state_dict.keys():
+		assert key in target_state_dict, f"key {key} not in target_state_dict"
+	# check that all Qs keys in target_state_dict are in new_state_dict
+	for key in target_state_dict.keys():
+		if 'Qs' in key:
+			assert key in new_state_dict, f"key {key} not in new_state_dict"
+	# check that source_state_dict contains no Qs keys
+	for key in source_state_dict.keys():
+		assert 'Qs' not in key, f"key {key} contains 'Qs'"
+
+	# copy log_std_min and log_std_max from target_state_dict to new_state_dict
+	new_state_dict['log_std_min'] = target_state_dict['log_std_min']
+	new_state_dict['log_std_dif'] = target_state_dict['log_std_dif']
+	new_state_dict['_action_masks'] = target_state_dict['_action_masks']
+
+	# copy new_state_dict to source_state_dict
+	source_state_dict.update(new_state_dict)
+
+	return source_state_dict
diff --git a/tdmpc2/common/logger.py b/tdmpc2/common/logger.py
index 4dce7ca..8ea2c2e 100755
--- a/tdmpc2/common/logger.py
+++ b/tdmpc2/common/logger.py
@@ -1,10 +1,11 @@
+import dataclasses
 import os
 import datetime
 import re
+
 import numpy as np
 import pandas as pd
 from termcolor import colored
-from omegaconf import OmegaConf
 
 from common import TASK_SET
 
@@ -116,7 +117,7 @@ class Logger:
 		print_run(cfg)
 		self.project = cfg.get("wandb_project", "none")
 		self.entity = cfg.get("wandb_entity", "none")
-		if cfg.disable_wandb or self.project == "none" or self.entity == "none":
+		if not cfg.enable_wandb or self.project == "none" or self.entity == "none":
 			print(colored("Wandb disabled.", "blue", attrs=["bold"]))
 			cfg.save_agent = False
 			cfg.save_video = False
@@ -133,7 +134,7 @@ class Logger:
 			group=self._group,
 			tags=cfg_to_group(cfg, return_list=True) + [f"seed:{cfg.seed}"],
 			dir=self._log_dir,
-			config=OmegaConf.to_container(cfg, resolve=True),
+			config=dataclasses.asdict(cfg),
 		)
 		print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
 		self._wandb = wandb
diff --git a/tdmpc2/common/math.py b/tdmpc2/common/math.py
index 62b8230..cc37800 100644
--- a/tdmpc2/common/math.py
+++ b/tdmpc2/common/math.py
@@ -9,43 +9,36 @@ def soft_ce(pred, target, cfg):
 	return -(target * pred).sum(-1, keepdim=True)
 
 
-@torch.jit.script
 def log_std(x, low, dif):
 	return low + 0.5 * dif * (torch.tanh(x) + 1)
 
 
-@torch.jit.script
-def _gaussian_residual(eps, log_std):
-	return -0.5 * eps.pow(2) - log_std
-
-
-@torch.jit.script
-def _gaussian_logprob(residual):
-	return residual - 0.5 * torch.log(2 * torch.pi)
-
-
-def gaussian_logprob(eps, log_std, size=None):
+def gaussian_logprob(eps, log_std):
 	"""Compute Gaussian log probability."""
-	residual = _gaussian_residual(eps, log_std).sum(-1, keepdim=True)
-	if size is None:
-		size = eps.size(-1)
-	return _gaussian_logprob(residual) * size
-
-
-@torch.jit.script
-def _squash(pi):
-	return torch.log(F.relu(1 - pi.pow(2)) + 1e-6)
+	residual = -0.5 * eps.pow(2) - log_std
+	log_prob = residual - 0.9189385175704956
+	return log_prob.sum(-1, keepdim=True)
 
 
 def squash(mu, pi, log_pi):
 	"""Apply squashing function."""
 	mu = torch.tanh(mu)
 	pi = torch.tanh(pi)
-	log_pi -= _squash(pi).sum(-1, keepdim=True)
+	squashed_pi = torch.log(F.relu(1 - pi.pow(2)) + 1e-6)
+	log_pi = log_pi - squashed_pi.sum(-1, keepdim=True)
 	return mu, pi, log_pi
 
 
-@torch.jit.script
+def int_to_one_hot(x, num_classes):
+	"""
+	Converts an integer tensor to a one-hot tensor.
+	Supports batched inputs.
+	"""
+	one_hot = torch.zeros(*x.shape, num_classes, device=x.device)
+	one_hot.scatter_(-1, x.unsqueeze(-1), 1)
+	return one_hot
+
+
 def symlog(x):
 	"""
 	Symmetric logarithmic function.
@@ -54,7 +47,6 @@ def symlog(x):
 	return torch.sign(x) * torch.log(1 + torch.abs(x))
 
 
-@torch.jit.script
 def symexp(x):
 	"""
 	Symmetric exponential function.
@@ -70,26 +62,33 @@ def two_hot(x, cfg):
 	elif cfg.num_bins == 1:
 		return symlog(x)
 	x = torch.clamp(symlog(x), cfg.vmin, cfg.vmax).squeeze(1)
-	bin_idx = torch.floor((x - cfg.vmin) / cfg.bin_size).long()
-	bin_offset = ((x - cfg.vmin) / cfg.bin_size - bin_idx.float()).unsqueeze(-1)
-	soft_two_hot = torch.zeros(x.size(0), cfg.num_bins, device=x.device)
-	soft_two_hot.scatter_(1, bin_idx.unsqueeze(1), 1 - bin_offset)
-	soft_two_hot.scatter_(1, (bin_idx.unsqueeze(1) + 1) % cfg.num_bins, bin_offset)
+	bin_idx = torch.floor((x - cfg.vmin) / cfg.bin_size)
+	bin_offset = ((x - cfg.vmin) / cfg.bin_size - bin_idx).unsqueeze(-1)
+	soft_two_hot = torch.zeros(x.shape[0], cfg.num_bins, device=x.device, dtype=x.dtype)
+	bin_idx = bin_idx.long()
+	soft_two_hot = soft_two_hot.scatter(1, bin_idx.unsqueeze(1), 1 - bin_offset)
+	soft_two_hot = soft_two_hot.scatter(1, (bin_idx.unsqueeze(1) + 1) % cfg.num_bins, bin_offset)
 	return soft_two_hot
 
 
-DREG_BINS = None
-
-
 def two_hot_inv(x, cfg):
 	"""Converts a batch of soft two-hot encoded vectors to scalars."""
-	global DREG_BINS
 	if cfg.num_bins == 0:
 		return x
 	elif cfg.num_bins == 1:
 		return symexp(x)
-	if DREG_BINS is None:
-		DREG_BINS = torch.linspace(cfg.vmin, cfg.vmax, cfg.num_bins, device=x.device)
+	dreg_bins = torch.linspace(cfg.vmin, cfg.vmax, cfg.num_bins, device=x.device, dtype=x.dtype)
 	x = F.softmax(x, dim=-1)
-	x = torch.sum(x * DREG_BINS, dim=-1, keepdim=True)
+	x = torch.sum(x * dreg_bins, dim=-1, keepdim=True)
 	return symexp(x)
+
+
+def gumbel_softmax_sample(p, temperature=1.0, dim=0):
+	logits = p.log()
+	# Generate Gumbel noise
+	gumbels = (
+		-torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log()
+	)  # ~Gumbel(0,1)
+	gumbels = (logits + gumbels) / temperature  # ~Gumbel(logits,tau)
+	y_soft = gumbels.softmax(dim)
+	return y_soft.argmax(-1)
diff --git a/tdmpc2/common/parser.py b/tdmpc2/common/parser.py
index ddce2b4..a8d9f25 100755
--- a/tdmpc2/common/parser.py
+++ b/tdmpc2/common/parser.py
@@ -1,5 +1,7 @@
+import dataclasses
 import re
 from pathlib import Path
+from typing import Any
 
 import hydra
 from omegaconf import OmegaConf
@@ -7,6 +9,23 @@ from omegaconf import OmegaConf
 from common import MODEL_SIZE, TASK_SET
 
 
+def cfg_to_dataclass(cfg, frozen=False):
+	"""
+	Converts an OmegaConf config to a dataclass object.
+	This prevents graph breaks when used with torch.compile.
+	"""
+	cfg_dict = OmegaConf.to_container(cfg)
+	fields = []
+	for key, value in cfg_dict.items():
+		fields.append((key, Any, dataclasses.field(default_factory=lambda value_=value: value_)))
+	dataclass_name = "Config"
+	dataclass = dataclasses.make_dataclass(dataclass_name, fields, frozen=frozen)
+	def get(self, val, default=None):
+		return getattr(self, val, default)
+	dataclass.get = get
+	return dataclass()
+
+
 def parse_cfg(cfg: OmegaConf) -> OmegaConf:
 	"""
 	Parses a Hydra config. Mostly for convenience.
@@ -53,9 +72,9 @@ def parse_cfg(cfg: OmegaConf) -> OmegaConf:
 	if cfg.multitask:
 		cfg.task_title = cfg.task.upper()
 		# Account for slight inconsistency in task_dim for the mt30 experiments
-		cfg.task_dim = 96 if cfg.task == 'mt80' or cfg.model_size in {1, 317} else 64
+		cfg.task_dim = 96 if cfg.task == 'mt80' or cfg.get('model_size', 5) in {1, 317} else 64
 	else:
 		cfg.task_dim = 0
 	cfg.tasks = TASK_SET.get(cfg.task, [cfg.task])
 
-	return cfg
+	return cfg_to_dataclass(cfg)
diff --git a/tdmpc2/common/scale.py b/tdmpc2/common/scale.py
index 63f0bb2..bee2019 100644
--- a/tdmpc2/common/scale.py
+++ b/tdmpc2/common/scale.py
@@ -1,48 +1,50 @@
 import torch
+from torch.nn import Buffer
 
 
-class RunningScale:
+class RunningScale(torch.nn.Module):
 	"""Running trimmed scale estimator."""
 
 	def __init__(self, cfg):
+		super().__init__()
 		self.cfg = cfg
-		self._value = torch.ones(1, dtype=torch.float32, device=torch.device('cuda'))
-		self._percentiles = torch.tensor([5, 95], dtype=torch.float32, device=torch.device('cuda'))
+		self.value = Buffer(torch.ones(1, dtype=torch.float32, device=torch.device('cuda:0')))
+		self._percentiles = Buffer(torch.tensor([5, 95], dtype=torch.float32, device=torch.device('cuda:0')))
 
 	def state_dict(self):
-		return dict(value=self._value, percentiles=self._percentiles)
+		return dict(value=self.value, percentiles=self._percentiles)
 
 	def load_state_dict(self, state_dict):
-		self._value.data.copy_(state_dict['value'])
-		self._percentiles.data.copy_(state_dict['percentiles'])
+		self.value.copy_(state_dict['value'])
+		self._percentiles.copy_(state_dict['percentiles'])
 
-	@property
-	def value(self):
-		return self._value.cpu().item()
+	def _positions(self, x_shape):
+		positions = self._percentiles * (x_shape-1) / 100
+		floored = torch.floor(positions)
+		ceiled = floored + 1
+		ceiled = torch.where(ceiled > x_shape - 1, x_shape - 1, ceiled)
+		weight_ceiled = positions-floored
+		weight_floored = 1.0 - weight_ceiled
+		return floored.long(), ceiled.long(), weight_floored.unsqueeze(1), weight_ceiled.unsqueeze(1)
 
 	def _percentile(self, x):
 		x_dtype, x_shape = x.dtype, x.shape
-		x = x.view(x.shape[0], -1)
-		in_sorted, _ = torch.sort(x, dim=0)
-		positions = self._percentiles * (x.shape[0]-1) / 100
-		floored = torch.floor(positions)
-		ceiled = floored + 1
-		ceiled[ceiled > x.shape[0] - 1] = x.shape[0] - 1
-		weight_ceiled = positions-floored
-		weight_floored = 1.0 - weight_ceiled
-		d0 = in_sorted[floored.long(), :] * weight_floored[:, None]
-		d1 = in_sorted[ceiled.long(), :] * weight_ceiled[:, None]
-		return (d0+d1).view(-1, *x_shape[1:]).type(x_dtype)
+		x = x.flatten(1, x.ndim-1)
+		in_sorted = torch.sort(x, dim=0).values
+		floored, ceiled, weight_floored, weight_ceiled = self._positions(x.shape[0])
+		d0 = in_sorted[floored] * weight_floored
+		d1 = in_sorted[ceiled] * weight_ceiled
+		return (d0+d1).reshape(-1, *x_shape[1:]).to(x_dtype)
 
 	def update(self, x):
 		percentiles = self._percentile(x.detach())
 		value = torch.clamp(percentiles[1] - percentiles[0], min=1.)
-		self._value.data.lerp_(value, self.cfg.tau)
+		self.value.data.lerp_(value, self.cfg.tau)
 
-	def __call__(self, x, update=False):
+	def forward(self, x, update=False):
 		if update:
 			self.update(x)
-		return x * (1/self.value)
+		return x / self.value
 
 	def __repr__(self):
 		return f'RunningScale(S: {self.value})'
diff --git a/tdmpc2/common/world_model.py b/tdmpc2/common/world_model.py
index 4bd4a48..34ef614 100644
--- a/tdmpc2/common/world_model.py
+++ b/tdmpc2/common/world_model.py
@@ -1,10 +1,11 @@
 from copy import deepcopy
 
-import numpy as np
 import torch
 import torch.nn as nn
 
 from common import layers, math, init
+from tensordict import TensorDict
+from tensordict.nn import TensorDictParams
 
 
 class WorldModel(nn.Module):
@@ -18,7 +19,7 @@ class WorldModel(nn.Module):
 		self.cfg = cfg
 		if cfg.multitask:
 			self._task_emb = nn.Embedding(len(cfg.tasks), cfg.task_dim, max_norm=1)
-			self._action_masks = torch.zeros(len(cfg.tasks), cfg.action_dim)
+			self.register_buffer("_action_masks", torch.zeros(len(cfg.tasks), cfg.action_dim))
 			for i in range(len(cfg.tasks)):
 				self._action_masks[i, :cfg.action_dims[i]] = 1.
 		self._encoder = layers.enc(cfg)
@@ -28,26 +29,46 @@ class WorldModel(nn.Module):
 		self._pi = layers.mlp(cfg.latent_dim + cfg.task_dim, 2*[cfg.mlp_dim], 2*cfg.action_dim)
 		self._Qs = layers.Ensemble([layers.mlp(cfg.latent_dim + cfg.action_dim + cfg.task_dim, 2*[cfg.mlp_dim], max(cfg.num_bins, 1), dropout=cfg.dropout) for _ in range(cfg.num_q)])
 		self.apply(init.weight_init)
-		init.zero_([self._reward[-1].weight, self._Qs.params[-2]])
-		self._target_Qs = deepcopy(self._Qs).requires_grad_(False)
-		self.log_std_min = torch.tensor(cfg.log_std_min)
-		self.log_std_dif = torch.tensor(cfg.log_std_max) - self.log_std_min
+		init.zero_([self._reward[-1].weight, self._Qs.params["2", "weight"]])
+
+		self.register_buffer("log_std_min", torch.tensor(cfg.log_std_min))
+		self.register_buffer("log_std_dif", torch.tensor(cfg.log_std_max) - self.log_std_min)
+		self.init()
+
+	def init(self):
+		# Create params
+		self._detach_Qs_params = TensorDictParams(self._Qs.params.data, no_convert=True)
+		self._target_Qs_params = TensorDictParams(self._Qs.params.data.clone(), no_convert=True)
+
+		# Create modules
+		with self._detach_Qs_params.data.to("meta").to_module(self._Qs.module):
+			self._detach_Qs = deepcopy(self._Qs)
+			self._target_Qs = deepcopy(self._Qs)
+
+		# Assign params to modules
+		# We do this strange assignment to avoid having duplicated tensors in the state-dict -- working on a better API for this
+		delattr(self._detach_Qs, "params")
+		self._detach_Qs.__dict__["params"] = self._detach_Qs_params
+		delattr(self._target_Qs, "params")
+		self._target_Qs.__dict__["params"] = self._target_Qs_params
+
+	def __repr__(self):
+		repr = 'TD-MPC2 World Model\n'
+		modules = ['Encoder', 'Dynamics', 'Reward', 'Policy prior', 'Q-functions']
+		for i, m in enumerate([self._encoder, self._dynamics, self._reward, self._pi, self._Qs]):
+			repr += f"{modules[i]}: {m}\n"
+		repr += "Learnable parameters: {:,}".format(self.total_params)
+		return repr
 
 	@property
 	def total_params(self):
 		return sum(p.numel() for p in self.parameters() if p.requires_grad)
-		
+
 	def to(self, *args, **kwargs):
-		"""
-		Overriding `to` method to also move additional tensors to device.
-		"""
 		super().to(*args, **kwargs)
-		if self.cfg.multitask:
-			self._action_masks = self._action_masks.to(*args, **kwargs)
-		self.log_std_min = self.log_std_min.to(*args, **kwargs)
-		self.log_std_dif = self.log_std_dif.to(*args, **kwargs)
+		self.init()
 		return self
-	
+
 	def train(self, mode=True):
 		"""
 		Overriding `train` method to keep target Q-networks in eval mode.
@@ -56,26 +77,12 @@ class WorldModel(nn.Module):
 		self._target_Qs.train(False)
 		return self
 
-	def track_q_grad(self, mode=True):
-		"""
-		Enables/disables gradient tracking of Q-networks.
-		Avoids unnecessary computation during policy optimization.
-		This method also enables/disables gradients for task embeddings.
-		"""
-		for p in self._Qs.parameters():
-			p.requires_grad_(mode)
-		if self.cfg.multitask:
-			for p in self._task_emb.parameters():
-				p.requires_grad_(mode)
-
 	def soft_update_target_Q(self):
 		"""
 		Soft-update target Q-networks using Polyak averaging.
 		"""
-		with torch.no_grad():
-			for p, p_target in zip(self._Qs.parameters(), self._target_Qs.parameters()):
-				p_target.data.lerp_(p.data, self.cfg.tau)
-	
+		self._target_Qs_params.lerp_(self._detach_Qs_params, self.cfg.tau)
+
 	def task_emb(self, x, task):
 		"""
 		Continuous task embedding for multi-task experiments.
@@ -110,7 +117,7 @@ class WorldModel(nn.Module):
 			z = self.task_emb(z, task)
 		z = torch.cat([z, a], dim=-1)
 		return self._dynamics(z)
-	
+
 	def reward(self, z, a, task):
 		"""
 		Predicts instantaneous (single-step) reward.
@@ -139,25 +146,39 @@ class WorldModel(nn.Module):
 			z = self.task_emb(z, task)
 
 		# Gaussian policy prior
-		mu, log_std = self._pi(z).chunk(2, dim=-1)
+		mean, log_std = self._pi(z).chunk(2, dim=-1)
 		log_std = math.log_std(log_std, self.log_std_min, self.log_std_dif)
-		eps = torch.randn_like(mu)
+		eps = torch.randn_like(mean)
 
 		if self.cfg.multitask: # Mask out unused action dimensions
-			mu = mu * self._action_masks[task]
+			mean = mean * self._action_masks[task]
 			log_std = log_std * self._action_masks[task]
 			eps = eps * self._action_masks[task]
 			action_dims = self._action_masks.sum(-1)[task].unsqueeze(-1)
 		else: # No masking
 			action_dims = None
 
-		log_pi = math.gaussian_logprob(eps, log_std, size=action_dims)
-		pi = mu + eps * log_std.exp()
-		mu, pi, log_pi = math.squash(mu, pi, log_pi)
+		log_prob = math.gaussian_logprob(eps, log_std)
 
-		return mu, pi, log_pi, log_std
+		# Scale log probability by action dimensions
+		size = eps.shape[-1] if action_dims is None else action_dims
+		scaled_log_prob = log_prob * size
 
-	def Q(self, z, a, task, return_type='min', target=False):
+		# Reparameterization trick
+		action = mean + eps * log_std.exp()
+		mean, action, log_prob = math.squash(mean, action, log_prob)
+
+		entropy_scale = scaled_log_prob / (log_prob + 1e-8)
+		info = TensorDict({
+			"mean": mean,
+			"log_std": log_std,
+			"action_prob": 1.,
+			"entropy": -log_prob,
+			"scaled_entropy": -log_prob * entropy_scale,
+		})
+		return action, info
+
+	def Q(self, z, a, task, return_type='min', target=False, detach=False):
 		"""
 		Predict state-action value.
 		`return_type` can be one of [`min`, `avg`, `all`]:
@@ -170,13 +191,21 @@ class WorldModel(nn.Module):
 
 		if self.cfg.multitask:
 			z = self.task_emb(z, task)
-			
+
 		z = torch.cat([z, a], dim=-1)
-		out = (self._target_Qs if target else self._Qs)(z)
+		if target:
+			qnet = self._target_Qs
+		elif detach:
+			qnet = self._detach_Qs
+		else:
+			qnet = self._Qs
+		out = qnet(z)
 
 		if return_type == 'all':
 			return out
 
-		Q1, Q2 = out[np.random.choice(self.cfg.num_q, 2, replace=False)]
-		Q1, Q2 = math.two_hot_inv(Q1, self.cfg), math.two_hot_inv(Q2, self.cfg)
-		return torch.min(Q1, Q2) if return_type == 'min' else (Q1 + Q2) / 2
+		qidx = torch.randperm(self.cfg.num_q, device=out.device)[:2]
+		Q = math.two_hot_inv(out[qidx], self.cfg)
+		if return_type == "min":
+			return Q.min(0).values
+		return Q.sum(0) / 2
diff --git a/tdmpc2/config.yaml b/tdmpc2/config.yaml
index 2288dfb..5f62907 100755
--- a/tdmpc2/config.yaml
+++ b/tdmpc2/config.yaml
@@ -67,7 +67,7 @@ simnorm_dim: 8
 wandb_project: ???
 wandb_entity: ???
 wandb_silent: false
-disable_wandb: true
+enable_wandb: true
 save_csv: true
 
 # misc
@@ -88,3 +88,6 @@ action_dims: ???
 episode_lengths: ???
 seed_steps: ???
 bin_size: ???
+
+# speedups
+compile: False
diff --git a/tdmpc2/envs/__init__.py b/tdmpc2/envs/__init__.py
index 6326a9e..7b1e0a3 100644
--- a/tdmpc2/envs/__init__.py
+++ b/tdmpc2/envs/__init__.py
@@ -1,19 +1,18 @@
 from copy import deepcopy
 import warnings
 
-import gym
+import gymnasium as gym
 
 from envs.wrappers.multitask import MultitaskWrapper
-from envs.wrappers.pixels import PixelWrapper
 from envs.wrappers.tensor import TensorWrapper
 
 def missing_dependencies(task):
 	raise ValueError(f'Missing dependencies for task {task}; install dependencies to use this environment.')
 
-try:
-	from envs.dmcontrol import make_env as make_dm_control_env
-except:
-	make_dm_control_env = missing_dependencies
+# try:
+from envs.dmcontrol import make_env as make_dm_control_env
+# except:
+	# make_dm_control_env = missing_dependencies
 try:
 	from envs.maniskill import make_env as make_maniskill_env
 except:
@@ -70,8 +69,6 @@ def make_env(cfg):
 		if env is None:
 			raise ValueError(f'Failed to make environment "{cfg.task}": please verify that dependencies are installed and that the task exists.')
 		env = TensorWrapper(env)
-	if cfg.get('obs', 'state') == 'rgb':
-		env = PixelWrapper(cfg, env)
 	try: # Dict
 		cfg.obs_shape = {k: v.shape for k, v in env.observation_space.spaces.items()}
 	except: # Box
diff --git a/tdmpc2/envs/dmcontrol.py b/tdmpc2/envs/dmcontrol.py
index bda699c..abfd7a7 100644
--- a/tdmpc2/envs/dmcontrol.py
+++ b/tdmpc2/envs/dmcontrol.py
@@ -1,183 +1,93 @@
-from collections import deque, defaultdict
-from typing import Any, NamedTuple
-import dm_env
+from collections import defaultdict, deque
+
+import gymnasium as gym
 import numpy as np
+import torch
+
 from envs.tasks import cheetah, walker, hopper, reacher, ball_in_cup, pendulum, fish
 from dm_control import suite
 suite.ALL_TASKS = suite.ALL_TASKS + suite._get_tasks('custom')
 suite.TASKS_BY_DOMAIN = suite._get_tasks_by_domain(suite.ALL_TASKS)
 from dm_control.suite.wrappers import action_scale
-from dm_env import StepType, specs
-import gym
+from envs.wrappers.timeout import Timeout
 
 from envs.wrappers.episodic import EpisodicWrapper
 
 
-class ExtendedTimeStep(NamedTuple):
-	step_type: Any
-	reward: Any
-	discount: Any
-	observation: Any
-	action: Any
-
-	def first(self):
-		return self.step_type == StepType.FIRST
-
-	def mid(self):
-		return self.step_type == StepType.MID
-
-	def last(self):
-		return self.step_type == StepType.LAST
+def get_obs_shape(env):
+	obs_shp = []
+	for v in env.observation_spec().values():
+		try:
+			shp = np.prod(v.shape)
+		except:
+			shp = 1
+		obs_shp.append(shp)
+	return (int(np.sum(obs_shp)),)
 
 
-class ActionRepeatWrapper(dm_env.Environment):
-	def __init__(self, env, num_repeats):
-		self._env = env
-		self._num_repeats = num_repeats
-
-	def step(self, action):
-		reward = 0.0
-		discount = 1.0
-		for i in range(self._num_repeats):
-			time_step = self._env.step(action)
-			reward += (time_step.reward or 0.0) * discount
-			discount *= time_step.discount
-			if time_step.last():
-				break
-
-		return time_step._replace(reward=reward, discount=discount)
-
-	def observation_spec(self):
-		return self._env.observation_spec()
-
-	def action_spec(self):
-		return self._env.action_spec()
-
-	def reset(self):
-		return self._env.reset()
-
-	def __getattr__(self, name):
-		return getattr(self._env, name)
-
-
-class ActionDTypeWrapper(dm_env.Environment):
-	def __init__(self, env, dtype):
-		self._env = env
-		wrapped_action_spec = env.action_spec()
-		self._action_spec = specs.BoundedArray(wrapped_action_spec.shape,
-											   dtype,
-											   wrapped_action_spec.minimum,
-											   wrapped_action_spec.maximum,
-											   'action')
-
-	def step(self, action):
-		action = action.astype(self._env.action_spec().dtype)
-		return self._env.step(action)
-
-	def observation_spec(self):
-		return self._env.observation_spec()
-
-	def action_spec(self):
-		return self._action_spec
-
-	def reset(self):
-		return self._env.reset()
-
-	def __getattr__(self, name):
-		return getattr(self._env, name)
-
-
-class ExtendedTimeStepWrapper(dm_env.Environment):
-	def __init__(self, env):
-		self._env = env
-
-	def reset(self):
-		time_step = self._env.reset()
-		return self._augment_time_step(time_step)
-
-	def step(self, action):
-		time_step = self._env.step(action)
-		return self._augment_time_step(time_step, action)
-
-	def _augment_time_step(self, time_step, action=None):
-		if action is None:
-			action_spec = self.action_spec()
-			action = np.zeros(action_spec.shape, dtype=action_spec.dtype)
-		return ExtendedTimeStep(observation=time_step.observation,
-								step_type=time_step.step_type,
-								action=action,
-								reward=time_step.reward or 0.0,
-								discount=time_step.discount or 1.0)
-
-	def observation_spec(self):
-		return self._env.observation_spec()
-
-	def action_spec(self):
-		return self._env.action_spec()
-
-	def __getattr__(self, name):
-		return getattr(self._env, name)
-
-
-class TimeStepToGymWrapper:
-	def __init__(self, env, domain, task):
-		obs_shp = []
-		for v in env.observation_spec().values():
-			try:
-				shp = np.prod(v.shape)
-			except:
-				shp = 1
-			obs_shp.append(shp)
-		obs_shp = (int(np.sum(obs_shp)),)
-		act_shp = env.action_spec().shape
-		self.observation_space = gym.spaces.Box(
-			low=np.full(
-				obs_shp,
-				-np.inf,
-				dtype=np.float32),
-			high=np.full(
-				obs_shp,
-				np.inf,
-				dtype=np.float32),
-			dtype=np.float32,
-		)
-		self.action_space = gym.spaces.Box(
-			low=np.full(act_shp, env.action_spec().minimum),
-			high=np.full(act_shp, env.action_spec().maximum),
-			dtype=env.action_spec().dtype)
+class DMControlWrapper:
+	def __init__(self, env, domain):
 		self.env = env
-		self.domain = domain
-		self.task = task
-		self.max_episode_steps = 500
-		self.t = 0
-	
+		self.camera_id = 2 if domain == 'quadruped' else 0
+		obs_shape = get_obs_shape(env)
+		action_shape = env.action_spec().shape
+		self.observation_space = gym.spaces.Box(
+			low=np.full(obs_shape, -np.inf, dtype=np.float32),
+			high=np.full(obs_shape, np.inf, dtype=np.float32),
+			dtype=np.float32)
+		self.action_space = gym.spaces.Box(
+			low=np.full(action_shape, env.action_spec().minimum),
+			high=np.full(action_shape, env.action_spec().maximum),
+			dtype=env.action_spec().dtype)
+		self.action_spec_dtype = env.action_spec().dtype
+
 	@property
 	def unwrapped(self):
 		return self.env
-
-	@property
-	def reward_range(self):
-		return None
-
-	@property
-	def metadata(self):
-		return None
 	
 	def _obs_to_array(self, obs):
-		return np.concatenate([v.flatten() for v in obs.values()])
+		return torch.from_numpy(
+			np.concatenate([v.flatten() for v in obs.values()], dtype=np.float32))
+	
+	def reset(self):
+		return self._obs_to_array(self.env.reset().observation)
+
+	def step(self, action):
+		reward = 0
+		action = action.astype(self.action_spec_dtype)
+		for _ in range(2):
+			step = self.env.step(action)
+			reward += step.reward
+		return self._obs_to_array(step.observation), reward, False, defaultdict(float)
+	
+	def render(self, width=384, height=384, camera_id=None):
+		return self.env.physics.render(height, width, camera_id or self.camera_id)
+
+
+class Pixels(gym.Wrapper):
+	def __init__(self, env, cfg, num_frames=3, size=64):
+		super().__init__(env)
+		self.cfg = cfg
+		self.env = env
+		self.observation_space = gym.spaces.Box(
+			low=0, high=255, shape=(num_frames*3, size, size), dtype=np.uint8)
+		self._frames = deque([], maxlen=num_frames)
+		self._size = size
+
+	def _get_obs(self, is_reset=False):
+		frame = self.env.render(width=self._size, height=self._size).transpose(2, 0, 1)
+		num_frames = self._frames.maxlen if is_reset else 1
+		for _ in range(num_frames):
+			self._frames.append(frame)
+		return torch.from_numpy(np.concatenate(self._frames))
 
 	def reset(self):
-		self.t = 0
-		return self._obs_to_array(self.env.reset().observation)
-	
-	def step(self, action):
-		self.t += 1
-		time_step = self.env.step(action)
-		return self._obs_to_array(time_step.observation), time_step.reward, time_step.last() or self.t == self.max_episode_steps, defaultdict(float)
+		self.env.reset()
+		return self._get_obs(is_reset=True)
 
-	def render(self, mode='rgb_array', width=384, height=384, camera_id=0):
-		camera_id = dict(quadruped=2).get(self.domain, camera_id)
-		return self.env.physics.render(height, width, camera_id)
+	def step(self, action):
+		_, reward, done, info = self.env.step(action)
+		return self._get_obs(), reward, done, info
 
 
 def make_env(cfg):
@@ -194,11 +104,9 @@ def make_env(cfg):
 					 task,
 					 task_kwargs={'random': cfg.seed},
 					 visualize_reward=False)
-	env = ActionDTypeWrapper(env, np.float32)
-	env = ActionRepeatWrapper(env, 2)
 	env = action_scale.Wrapper(env, minimum=-1., maximum=1.)
-	env = ExtendedTimeStepWrapper(env)
-	env = TimeStepToGymWrapper(env, domain, task)
-	if cfg.episodic:
-		env = EpisodicWrapper(cfg, env)
+	env = DMControlWrapper(env, domain)
+	if cfg.obs == 'rgb':
+		env = Pixels(env, cfg)
+	env = Timeout(env, max_episode_steps=500)
 	return env
diff --git a/tdmpc2/envs/maniskill.py b/tdmpc2/envs/maniskill.py
index 4a37b6d..25d5937 100644
--- a/tdmpc2/envs/maniskill.py
+++ b/tdmpc2/envs/maniskill.py
@@ -1,6 +1,6 @@
-import gym
+import gymnasium as gym
 import numpy as np
-from envs.wrappers.time_limit import TimeLimit
+from envs.wrappers.timeout import Timeout
 
 import mani_skill2.envs
 
@@ -77,6 +77,6 @@ def make_env(cfg):
 		render_camera_cfgs=dict(width=384, height=384),
 	)
 	env = ManiSkillWrapper(env, cfg)
-	env = TimeLimit(env, max_episode_steps=100)
+	env = Timeout(env, max_episode_steps=100)
 	env.max_episode_steps = env._max_episode_steps
 	return env
diff --git a/tdmpc2/envs/metaworld.py b/tdmpc2/envs/metaworld.py
index f5f4f0d..f9b3513 100644
--- a/tdmpc2/envs/metaworld.py
+++ b/tdmpc2/envs/metaworld.py
@@ -1,6 +1,6 @@
 import numpy as np
 import gym
-from envs.wrappers.time_limit import TimeLimit
+from envs.wrappers.timeout import Timeout
 
 from metaworld.envs import ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE
 
@@ -47,6 +47,6 @@ def make_env(cfg):
 	assert cfg.obs == 'state', 'This task only supports state observations.'
 	env = ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE[env_id](seed=cfg.seed)
 	env = MetaWorldWrapper(env, cfg)
-	env = TimeLimit(env, max_episode_steps=100)
+	env = Timeout(env, max_episode_steps=100)
 	env.max_episode_steps = env._max_episode_steps
 	return env
diff --git a/tdmpc2/envs/myosuite.py b/tdmpc2/envs/myosuite.py
index fa6876e..ee35fd1 100644
--- a/tdmpc2/envs/myosuite.py
+++ b/tdmpc2/envs/myosuite.py
@@ -1,6 +1,6 @@
 import numpy as np
-import gym
-from envs.wrappers.time_limit import TimeLimit
+import gymnasium as gym
+from envs.wrappers.timeout import Timeout
 
 
 MYOSUITE_TASKS = {
@@ -24,9 +24,11 @@ class MyoSuiteWrapper(gym.Wrapper):
 		self.cfg = cfg
 		self.camera_id = 'hand_side_inter'
 
+	def reset(self):
+		return self.env.reset()[0]
+
 	def step(self, action):
-		obs, reward, _, info = self.env.step(action.copy())
-		obs = obs.astype(np.float32)
+		obs, reward, _, _, info = self.env.step(action.copy())
 		info['success'] = info['solved']
 		return obs, reward, False, info
 
@@ -48,8 +50,9 @@ def make_env(cfg):
 		raise ValueError('Unknown task:', cfg.task)
 	assert cfg.obs == 'state', 'This task only supports state observations.'
 	import myosuite
-	env = gym.make(MYOSUITE_TASKS[cfg.task])
+	from myosuite.utils import gym as gym_utils
+	env = gym_utils.make(MYOSUITE_TASKS[cfg.task])
 	env = MyoSuiteWrapper(env, cfg)
-	env = TimeLimit(env, max_episode_steps=100)
+	env = Timeout(env, max_episode_steps=100)
 	env.max_episode_steps = env._max_episode_steps
 	return env
diff --git a/tdmpc2/envs/wrappers/episodic.py b/tdmpc2/envs/wrappers/episodic.py
index f27c8fc..b0d4794 100644
--- a/tdmpc2/envs/wrappers/episodic.py
+++ b/tdmpc2/envs/wrappers/episodic.py
@@ -1,6 +1,6 @@
 from collections import deque
 
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 
diff --git a/tdmpc2/envs/wrappers/multitask.py b/tdmpc2/envs/wrappers/multitask.py
index 08dd4eb..529295a 100644
--- a/tdmpc2/envs/wrappers/multitask.py
+++ b/tdmpc2/envs/wrappers/multitask.py
@@ -1,4 +1,4 @@
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 
diff --git a/tdmpc2/envs/wrappers/pixels.py b/tdmpc2/envs/wrappers/pixels.py
deleted file mode 100644
index c299875..0000000
--- a/tdmpc2/envs/wrappers/pixels.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from collections import deque
-
-import gym
-import numpy as np
-import torch
-
-
-class PixelWrapper(gym.Wrapper):
-	"""
-	Wrapper for pixel observations. Compatible with DMControl environments.
-	"""
-
-	def __init__(self, cfg, env, num_frames=3, render_size=64):
-		super().__init__(env)
-		self.cfg = cfg
-		self.env = env
-		self.observation_space = gym.spaces.Box(
-			low=0, high=255, shape=(num_frames*3, render_size, render_size), dtype=np.uint8
-		)
-		self._frames = deque([], maxlen=num_frames)
-		self._render_size = render_size
-
-	def _get_obs(self):
-		frame = self.env.render(
-			mode='rgb_array', width=self._render_size, height=self._render_size
-		).transpose(2, 0, 1)
-		self._frames.append(frame)
-		return torch.from_numpy(np.concatenate(self._frames))
-
-	def reset(self):
-		self.env.reset()
-		for _ in range(self._frames.maxlen):
-			obs = self._get_obs()
-		return obs
-
-	def step(self, action):
-		_, reward, done, info = self.env.step(action)
-		return self._get_obs(), reward, done, info
diff --git a/tdmpc2/envs/wrappers/tensor.py b/tdmpc2/envs/wrappers/tensor.py
index 81bed19..4a6819a 100644
--- a/tdmpc2/envs/wrappers/tensor.py
+++ b/tdmpc2/envs/wrappers/tensor.py
@@ -1,6 +1,6 @@
 from collections import defaultdict
 
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 
@@ -17,9 +17,10 @@ class TensorWrapper(gym.Wrapper):
 		return torch.from_numpy(self.action_space.sample().astype(np.float32))
 
 	def _try_f32_tensor(self, x):
-		x = torch.from_numpy(x)
-		if x.dtype == torch.float64:
-			x = x.float()
+		if isinstance(x, np.ndarray):
+			x = torch.from_numpy(x)
+			if x.dtype == torch.float64:
+				x = x.float()
 		return x
 
 	def _obs_to_tensor(self, obs):
diff --git a/tdmpc2/envs/wrappers/time_limit.py b/tdmpc2/envs/wrappers/time_limit.py
deleted file mode 100644
index f81c281..0000000
--- a/tdmpc2/envs/wrappers/time_limit.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-Wrapper for limiting the time steps of an environment.
-Source: https://github.com/openai/gym/blob/3498617bf031538a808b75b932f4ed2c11896a3e/gym/wrappers/time_limit.py
-"""
-from typing import Optional
-
-import gym
-
-
-class TimeLimit(gym.Wrapper):
-    """This wrapper will issue a `done` signal if a maximum number of timesteps is exceeded.
-
-    Oftentimes, it is **very** important to distinguish `done` signals that were produced by the
-    :class:`TimeLimit` wrapper (truncations) and those that originate from the underlying environment (terminations).
-    This can be done by looking at the ``info`` that is returned when `done`-signal was issued.
-    The done-signal originates from the time limit (i.e. it signifies a *truncation*) if and only if
-    the key `"TimeLimit.truncated"` exists in ``info`` and the corresponding value is ``True``.
-
-    Example:
-       >>> from gym.envs.classic_control import CartPoleEnv
-       >>> from gym.wrappers import TimeLimit
-       >>> env = CartPoleEnv()
-       >>> env = TimeLimit(env, max_episode_steps=1000)
-    """
-
-    def __init__(self, env: gym.Env, max_episode_steps: Optional[int] = None):
-        """Initializes the :class:`TimeLimit` wrapper with an environment and the number of steps after which truncation will occur.
-
-        Args:
-            env: The environment to apply the wrapper
-            max_episode_steps: An optional max episode steps (if ``Ǹone``, ``env.spec.max_episode_steps`` is used)
-        """
-        super().__init__(env)
-        if max_episode_steps is None and self.env.spec is not None:
-            max_episode_steps = env.spec.max_episode_steps
-        if self.env.spec is not None:
-            self.env.spec.max_episode_steps = max_episode_steps
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = None
-
-    def step(self, action):
-        """Steps through the environment and if the number of steps elapsed exceeds ``max_episode_steps`` then truncate.
-
-        Args:
-            action: The environment step action
-
-        Returns:
-            The environment step ``(observation, reward, done, info)`` with "TimeLimit.truncated"=True
-            when truncated (the number of steps elapsed >= max episode steps) or
-            "TimeLimit.truncated"=False if the environment terminated
-        """
-        observation, reward, done, info = self.env.step(action)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            # TimeLimit.truncated key may have been already set by the environment
-            # do not overwrite it
-            episode_truncated = not done or info.get("TimeLimit.truncated", False)
-            info["TimeLimit.truncated"] = episode_truncated
-            done = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        """Resets the environment with :param:`**kwargs` and sets the number of steps elapsed to zero.
-
-        Args:
-            **kwargs: The kwargs to reset the environment with
-
-        Returns:
-            The reset environment
-        """
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
diff --git a/tdmpc2/envs/wrappers/timeout.py b/tdmpc2/envs/wrappers/timeout.py
new file mode 100644
index 0000000..cc2081c
--- /dev/null
+++ b/tdmpc2/envs/wrappers/timeout.py
@@ -0,0 +1,25 @@
+import gymnasium as gym
+
+
+class Timeout(gym.Wrapper):
+	"""
+	Wrapper for enforcing a time limit on the environment.
+	"""
+
+	def __init__(self, env, max_episode_steps):
+		super().__init__(env)
+		self._max_episode_steps = max_episode_steps
+	
+	@property
+	def max_episode_steps(self):
+		return self._max_episode_steps
+
+	def reset(self, **kwargs):
+		self._t = 0
+		return self.env.reset(**kwargs)
+
+	def step(self, action):
+		obs, reward, done, info = self.env.step(action)
+		self._t += 1
+		done = done or self._t >= self.max_episode_steps
+		return obs, reward, done, info
diff --git a/tdmpc2/evaluate.py b/tdmpc2/evaluate.py
index a9f04ea..9c788a2 100755
--- a/tdmpc2/evaluate.py
+++ b/tdmpc2/evaluate.py
@@ -1,5 +1,5 @@
 import os
-os.environ['MUJOCO_GL'] = 'egl'
+os.environ['MUJOCO_GL'] = os.getenv("MUJOCO_GL", 'egl')
 import warnings
 warnings.filterwarnings('ignore')
 
diff --git a/tdmpc2/tdmpc2.py b/tdmpc2/tdmpc2.py
index c442f4e..6f44e15 100755
--- a/tdmpc2/tdmpc2.py
+++ b/tdmpc2/tdmpc2.py
@@ -1,13 +1,14 @@
-import numpy as np
 import torch
 import torch.nn.functional as F
 
 from common import math
 from common.scale import RunningScale
 from common.world_model import WorldModel
+from common.layers import api_model_conversion
+from tensordict import TensorDict
 
 
-class TDMPC2:
+class TDMPC2(torch.nn.Module):
 	"""
 	TD-MPC2 agent. Implements training + inference.
 	Can be used for both single-task and multi-task experiments,
@@ -15,8 +16,9 @@ class TDMPC2:
 	"""
 
 	def __init__(self, cfg):
+		super().__init__()
 		self.cfg = cfg
-		self.device = torch.device('cuda')
+		self.device = torch.device('cuda:0')
 		self.model = WorldModel(cfg).to(self.device)
 		self.optim = torch.optim.Adam([
 			{'params': self.model._encoder.parameters(), 'lr': self.cfg.lr*self.cfg.enc_lr_scale},
@@ -24,15 +26,32 @@ class TDMPC2:
 			{'params': self.model._reward.parameters()},
 			{'params': self.model._terminated.parameters()},
 			{'params': self.model._Qs.parameters()},
-			{'params': self.model._task_emb.parameters() if self.cfg.multitask else []}
-		], lr=self.cfg.lr)
-		self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr, eps=1e-5)
+			{'params': self.model._task_emb.parameters() if self.cfg.multitask else []
+			 }
+		], lr=self.cfg.lr, capturable=True)
+		self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr, eps=1e-5, capturable=True)
 		self.model.eval()
 		self.scale = RunningScale(cfg)
 		self.cfg.iterations += 2*int(cfg.action_dim >= 20) # Heuristic for large action spaces
 		self.discount = torch.tensor(
-			[self._get_discount(ep_len) for ep_len in cfg.episode_lengths], device='cuda'
+			[self._get_discount(ep_len) for ep_len in cfg.episode_lengths], device='cuda:0'
 		) if self.cfg.multitask else self._get_discount(cfg.episode_length)
+		self._prev_mean = torch.nn.Buffer(torch.zeros(self.cfg.horizon, self.cfg.action_dim, device=self.device))
+		if cfg.compile:
+			print('Compiling update function with torch.compile...')
+			self._update = torch.compile(self._update, mode="reduce-overhead")
+
+	@property
+	def plan(self):
+		_plan_val = getattr(self, "_plan_val", None)
+		if _plan_val is not None:
+			return _plan_val
+		if self.cfg.compile:
+			plan = torch.compile(self._plan, mode="reduce-overhead")
+		else:
+			plan = self._plan
+		self._plan_val = plan
+		return self._plan_val
 
 	def _get_discount(self, episode_length):
 		"""
@@ -52,7 +71,7 @@ class TDMPC2:
 	def save(self, fp):
 		"""
 		Save state dict of the agent to filepath.
-		
+
 		Args:
 			fp (str): Filepath to save state dict to.
 		"""
@@ -61,36 +80,43 @@ class TDMPC2:
 	def load(self, fp):
 		"""
 		Load a saved state dict from filepath (or dictionary) into current agent.
-		
+
 		Args:
 			fp (str or dict): Filepath or state dict to load.
 		"""
-		state_dict = fp if isinstance(fp, dict) else torch.load(fp)
-		self.model.load_state_dict(state_dict["model"])
+		if isinstance(fp, dict):
+			state_dict = fp
+		else:
+			state_dict = torch.load(fp, map_location=torch.get_default_device(), weights_only=False)
+		state_dict = state_dict["model"] if "model" in state_dict else state_dict
+		state_dict = api_model_conversion(self.model.state_dict(), state_dict)
+		self.model.load_state_dict(state_dict)
+		return
 
 	@torch.no_grad()
 	def act(self, obs, t0=False, eval_mode=False, task=None):
 		"""
 		Select an action by planning in the latent space of the world model.
-		
+
 		Args:
 			obs (torch.Tensor): Observation from the environment.
 			t0 (bool): Whether this is the first observation in the episode.
 			eval_mode (bool): Whether to use the mean of the action distribution.
 			task (int): Task index (only used for multi-task experiments).
-		
+
 		Returns:
 			torch.Tensor: Action to take in the environment.
 		"""
 		obs = obs.to(self.device, non_blocking=True).unsqueeze(0)
 		if task is not None:
 			task = torch.tensor([task], device=self.device)
-		z = self.model.encode(obs, task)
 		if self.cfg.mpc:
-			a = self.plan(z, t0=t0, eval_mode=eval_mode, task=task)
-		else:
-			a = self.model.pi(z, task)[int(not eval_mode)][0]
-		return a.cpu()
+			return self.plan(obs, t0=t0, eval_mode=eval_mode, task=task).cpu()
+		z = self.model.encode(obs, task)
+		action, info = self.model.pi(z, task)
+		if eval_mode:
+			action = info["mean"]
+		return action[0].cpu()
 
 	@torch.no_grad()
 	def _estimate_value(self, z, actions, task):
@@ -100,16 +126,19 @@ class TDMPC2:
 		for t in range(self.cfg.horizon):
 			reward = math.two_hot_inv(self.model.reward(z, actions[t], task), self.cfg)
 			z = self.model.next(z, actions[t], task)
-			G += discount * (1-terminated) * reward
-			discount *= self.discount[torch.tensor(task)] if self.cfg.multitask else self.discount
-			terminated = torch.clip_(terminated + (self.model.terminated(z, task) > 0.5).float(), max=1.)
-		return G + discount * (1-terminated) * self.model.Q(z, self.model.pi(z, task)[1], task, return_type='avg')
+
+			G = G + discount * (1-terminated) * reward
+			discount_update = self.discount[torch.tensor(task)] if self.cfg.multitask else self.discount
+			discount = discount * discount_update
+			terminated = torch.clip(terminated + (self.model.terminated(z, task) > 0.5).float(), max=1.)
+		action, _ = self.model.pi(z, task)
+		return G + discount * (1-terminated) * self.model.Q(z, action, task, return_type='avg')
 
 	@torch.no_grad()
-	def plan(self, z, t0=False, eval_mode=False, task=None):
+	def _plan(self, obs, t0=False, eval_mode=False, task=None):
 		"""
 		Plan a sequence of actions using the learned world model.
-		
+
 		Args:
 			z (torch.Tensor): Latent state from which to plan.
 			t0 (bool): Whether this is the first observation in the episode.
@@ -118,65 +147,67 @@ class TDMPC2:
 
 		Returns:
 			torch.Tensor: Action to take in the environment.
-		"""		
+		"""
 		# Sample policy trajectories
+		z = self.model.encode(obs, task)
 		if self.cfg.num_pi_trajs > 0:
 			pi_actions = torch.empty(self.cfg.horizon, self.cfg.num_pi_trajs, self.cfg.action_dim, device=self.device)
 			_z = z.repeat(self.cfg.num_pi_trajs, 1)
 			for t in range(self.cfg.horizon-1):
-				pi_actions[t] = self.model.pi(_z, task)[1]
+				pi_actions[t], _ = self.model.pi(_z, task)
 				_z = self.model.next(_z, pi_actions[t], task)
-			pi_actions[-1] = self.model.pi(_z, task)[1]
+			pi_actions[-1], _ = self.model.pi(_z, task)
 
 		# Initialize state and parameters
 		z = z.repeat(self.cfg.num_samples, 1)
 		mean = torch.zeros(self.cfg.horizon, self.cfg.action_dim, device=self.device)
-		std = self.cfg.max_std*torch.ones(self.cfg.horizon, self.cfg.action_dim, device=self.device)
+		std = torch.full((self.cfg.horizon, self.cfg.action_dim), self.cfg.max_std, dtype=torch.float, device=self.device)
 		if not t0:
 			mean[:-1] = self._prev_mean[1:]
 		actions = torch.empty(self.cfg.horizon, self.cfg.num_samples, self.cfg.action_dim, device=self.device)
 		if self.cfg.num_pi_trajs > 0:
 			actions[:, :self.cfg.num_pi_trajs] = pi_actions
-	
+
 		# Iterate MPPI
 		for _ in range(self.cfg.iterations):
 
 			# Sample actions
-			actions[:, self.cfg.num_pi_trajs:] = (mean.unsqueeze(1) + std.unsqueeze(1) * \
-				torch.randn(self.cfg.horizon, self.cfg.num_samples-self.cfg.num_pi_trajs, self.cfg.action_dim, device=std.device)) \
-				.clamp(-1, 1)
+			r = torch.randn(self.cfg.horizon, self.cfg.num_samples-self.cfg.num_pi_trajs, self.cfg.action_dim, device=std.device)
+			actions_sample = mean.unsqueeze(1) + std.unsqueeze(1) * r
+			actions_sample = actions_sample.clamp(-1, 1)
+			actions[:, self.cfg.num_pi_trajs:] = actions_sample
 			if self.cfg.multitask:
 				actions = actions * self.model._action_masks[task]
 
 			# Compute elite actions
-			value = self._estimate_value(z, actions, task).nan_to_num_(0)
+			value = self._estimate_value(z, actions, task).nan_to_num(0)
 			elite_idxs = torch.topk(value.squeeze(1), self.cfg.num_elites, dim=0).indices
 			elite_value, elite_actions = value[elite_idxs], actions[:, elite_idxs]
 
 			# Update parameters
-			max_value = elite_value.max(0)[0]
+			max_value = elite_value.max(0).values
 			score = torch.exp(self.cfg.temperature*(elite_value - max_value))
-			score /= score.sum(0)
-			mean = torch.sum(score.unsqueeze(0) * elite_actions, dim=1) / (score.sum(0) + 1e-9)
-			std = torch.sqrt(torch.sum(score.unsqueeze(0) * (elite_actions - mean.unsqueeze(1)) ** 2, dim=1) / (score.sum(0) + 1e-9)) \
-				.clamp_(self.cfg.min_std, self.cfg.max_std)
+			score = score / score.sum(0)
+			mean = (score.unsqueeze(0) * elite_actions).sum(dim=1) / (score.sum(0) + 1e-9)
+			std = ((score.unsqueeze(0) * (elite_actions - mean.unsqueeze(1)) ** 2).sum(dim=1) / (score.sum(0) + 1e-9)).sqrt()
+			std = std.clamp(self.cfg.min_std, self.cfg.max_std)
 			if self.cfg.multitask:
 				mean = mean * self.model._action_masks[task]
 				std = std * self.model._action_masks[task]
 
 		# Select action
-		score = score.squeeze(1).cpu().numpy()
-		actions = elite_actions[:, np.random.choice(np.arange(score.shape[0]), p=score)]
-		self._prev_mean = mean
+		rand_idx = math.gumbel_softmax_sample(score.squeeze(1))  # gumbel_softmax_sample is compatible with cuda graphs
+		actions = torch.index_select(elite_actions, 1, rand_idx).squeeze(1)
 		a, std = actions[0], std[0]
 		if not eval_mode:
-			a += std * torch.randn(self.cfg.action_dim, device=std.device)
-		return a.clamp_(-1, 1)
-		
+			a = a + std * torch.randn(self.cfg.action_dim, device=std.device)
+		self._prev_mean.copy_(mean)
+		return a.clamp(-1, 1)
+
 	def update_pi(self, zs, task):
 		"""
 		Update policy using a sequence of latent states.
-		
+
 		Args:
 			zs (torch.Tensor): Sequence of latent states.
 			task (torch.Tensor): Task index (only used for multi-task experiments).
@@ -184,60 +215,53 @@ class TDMPC2:
 		Returns:
 			float: Loss of the policy update.
 		"""
-		self.pi_optim.zero_grad(set_to_none=True)
-		self.model.track_q_grad(False)
-		_, pis, log_pis, _ = self.model.pi(zs, task)
-		qs = self.model.Q(zs, pis, task, return_type='avg')
+		action, info = self.model.pi(zs, task)
+		qs = self.model.Q(zs, action, task, return_type='avg', detach=True)
 		self.scale.update(qs[0])
 		qs = self.scale(qs)
 
 		# Loss is a weighted sum of Q-values
 		rho = torch.pow(self.cfg.rho, torch.arange(len(qs), device=self.device))
-		pi_loss = ((self.cfg.entropy_coef * log_pis - qs).mean(dim=(1,2)) * rho).mean()
+		pi_loss = (-(self.cfg.entropy_coef * info["scaled_entropy"] + qs).mean(dim=(1,2)) * rho).mean()
 		pi_loss.backward()
-		torch.nn.utils.clip_grad_norm_(self.model._pi.parameters(), self.cfg.grad_clip_norm)
+		pi_grad_norm = torch.nn.utils.clip_grad_norm_(self.model._pi.parameters(), self.cfg.grad_clip_norm)
 		self.pi_optim.step()
-		self.model.track_q_grad(True)
+		self.pi_optim.zero_grad(set_to_none=True)
 
-		return pi_loss.item()
+		info = TensorDict({
+			"pi_loss": pi_loss,
+			"pi_grad_norm": pi_grad_norm,
+			"pi_entropy": info["entropy"],
+			"pi_scaled_entropy": info["scaled_entropy"],
+			"pi_scale": self.scale.value,
+		})
+		return info
 
 	@torch.no_grad()
 	def _td_target(self, next_z, reward, terminated, task):
 		"""
 		Compute the TD-target from a reward and the observation at the following time step.
-		
+
 		Args:
 			next_z (torch.Tensor): Latent state at the following time step.
 			reward (torch.Tensor): Reward at the current time step.
 			terminated (torch.Tensor): Termination signal at the current time step.
 			task (torch.Tensor): Task index (only used for multi-task experiments).
-		
+
 		Returns:
 			torch.Tensor: TD-target.
 		"""
-		pi = self.model.pi(next_z, task)[1]
+		action, _ = self.model.pi(next_z, task)
 		discount = self.discount[task].unsqueeze(-1) if self.cfg.multitask else self.discount
-		return reward + discount * (1-terminated) * self.model.Q(next_z, pi, task, return_type='min', target=True)
+		return reward + discount * (1-terminated) * self.model.Q(next_z, action, task, return_type='min', target=True)
 
-	def update(self, buffer):
-		"""
-		Main update function. Corresponds to one iteration of model learning.
-		
-		Args:
-			buffer (common.buffer.Buffer): Replay buffer.
-		
-		Returns:
-			dict: Dictionary of training statistics.
-		"""
-		obs, action, reward, terminated, task = buffer.sample()
-		
+	def _update(self, obs, action, reward, terminated, task=None):
 		# Compute targets
 		with torch.no_grad():
 			next_z = self.model.encode(obs[1:], task)
 			td_targets = self._td_target(next_z, reward, terminated, task)
 
 		# Prepare for update
-		self.optim.zero_grad(set_to_none=True)
 		self.model.train()
 
 		# Latent rollout
@@ -245,9 +269,9 @@ class TDMPC2:
 		z = self.model.encode(obs[0], task)
 		zs[0] = z
 		consistency_loss = 0
-		for t in range(self.cfg.horizon):
-			z = self.model.next(z, action[t], task)
-			consistency_loss += F.mse_loss(z, next_z[t]) * self.cfg.rho**t
+		for t, (_action, _next_z) in enumerate(zip(action.unbind(0), next_z.unbind(0))):
+			z = self.model.next(z, _action, task)
+			consistency_loss = consistency_loss + F.mse_loss(z, _next_z) * self.cfg.rho**t
 			zs[t+1] = z
 
 		# Predictions
@@ -258,14 +282,15 @@ class TDMPC2:
 
 		# Compute losses
 		reward_loss, value_loss = 0, 0
-		for t in range(self.cfg.horizon):
-			reward_loss += math.soft_ce(reward_preds[t], reward[t], self.cfg).mean() * self.cfg.rho**t
-			for q in range(self.cfg.num_q):
-				value_loss += math.soft_ce(qs[q][t], td_targets[t], self.cfg).mean() * self.cfg.rho**t
-		terminated_loss = F.binary_cross_entropy(terminated_pred, terminated)
-		consistency_loss *= (1/self.cfg.horizon)
-		reward_loss *= (1/self.cfg.horizon)
-		value_loss *= (1/(self.cfg.horizon * self.cfg.num_q))
+		for t, (rew_pred_unbind, rew_unbind, td_targets_unbind, qs_unbind) in enumerate(zip(reward_preds.unbind(0), reward.unbind(0), td_targets.unbind(0), qs.unbind(1))):
+			reward_loss = reward_loss + math.soft_ce(rew_pred_unbind, rew_unbind, self.cfg).mean() * self.cfg.rho**t
+			for _, qs_unbind_unbind in enumerate(qs_unbind.unbind(0)):
+				value_loss = value_loss + math.soft_ce(qs_unbind_unbind, td_targets_unbind, self.cfg).mean() * self.cfg.rho**t
+
+		consistency_loss = consistency_loss / self.cfg.horizon
+		reward_loss = reward_loss / self.cfg.horizon
+		terminated_loss = F.binary_cross_entropy(terminated_pred, terminated[-1])
+		value_loss = value_loss / (self.cfg.horizon * self.cfg.num_q)
 		total_loss = (
 			self.cfg.consistency_coef * consistency_loss +
 			self.cfg.reward_coef * reward_loss +
@@ -277,22 +302,39 @@ class TDMPC2:
 		total_loss.backward()
 		grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip_norm)
 		self.optim.step()
+		self.optim.zero_grad(set_to_none=True)
 
 		# Update policy
-		pi_loss = self.update_pi(zs.detach(), task)
+		pi_info = self.update_pi(zs.detach(), task)
 
 		# Update target Q-functions
 		self.model.soft_update_target_Q()
 
 		# Return training statistics
 		self.model.eval()
-		return {
-			"consistency_loss": float(consistency_loss.mean().item()),
-			"reward_loss": float(reward_loss.mean().item()),
-			"terminated_loss": float(terminated_loss.mean().item()),
-			"value_loss": float(value_loss.mean().item()),
-			"pi_loss": pi_loss,
-			"total_loss": float(total_loss.mean().item()),
-			"grad_norm": float(grad_norm),
-			"pi_scale": float(self.scale.value),
-		}
+		info = TensorDict({
+			"consistency_loss": consistency_loss,
+			"reward_loss": reward_loss,
+			"value_loss": value_loss,
+			"total_loss": total_loss,
+			"grad_norm": grad_norm,
+		})
+		info.update(pi_info)
+		return info.detach().mean()
+
+	def update(self, buffer):
+		"""
+		Main update function. Corresponds to one iteration of model learning.
+
+		Args:
+			buffer (common.buffer.Buffer): Replay buffer.
+
+		Returns:
+			dict: Dictionary of training statistics.
+		"""
+		obs, action, reward, terminated, task = buffer.sample()
+		kwargs = {}
+		if task is not None:
+			kwargs["task"] = task
+		torch.compiler.cudagraph_mark_step_begin()
+		return self._update(obs, action, reward, terminated, **kwargs)
diff --git a/tdmpc2/train.py b/tdmpc2/train.py
index 1680871..09813fa 100755
--- a/tdmpc2/train.py
+++ b/tdmpc2/train.py
@@ -1,6 +1,8 @@
 import os
-os.environ['MUJOCO_GL'] = 'egl'
+os.environ['MUJOCO_GL'] = os.getenv("MUJOCO_GL", 'egl')
 os.environ['LAZY_LEGACY_OP'] = '0'
+os.environ['TORCHDYNAMO_INLINE_INBUILT_NN_MODULES'] = "1"
+os.environ['TORCH_LOGS'] = "+recompiles"
 import warnings
 warnings.filterwarnings('ignore')
 import torch
@@ -18,6 +20,7 @@ from trainer.online_trainer import OnlineTrainer
 from common.logger import Logger
 
 torch.backends.cudnn.benchmark = True
+torch.set_float32_matmul_precision('high')
 
 
 @hydra.main(config_name='config', config_path='.')
diff --git a/tdmpc2/trainer/base.py b/tdmpc2/trainer/base.py
index 27a328d..6d14783 100755
--- a/tdmpc2/trainer/base.py
+++ b/tdmpc2/trainer/base.py
@@ -8,7 +8,6 @@ class Trainer:
 		self.buffer = buffer
 		self.logger = logger
 		print('Architecture:', self.agent.model)
-		print("Learnable parameters: {:,}".format(self.agent.model.total_params))
 
 	def eval(self):
 		"""Evaluate a TD-MPC2 agent."""
diff --git a/tdmpc2/trainer/offline_trainer.py b/tdmpc2/trainer/offline_trainer.py
index 1bace8e..a46d00b 100755
--- a/tdmpc2/trainer/offline_trainer.py
+++ b/tdmpc2/trainer/offline_trainer.py
@@ -27,6 +27,7 @@ class OfflineTrainer(Trainer):
 			for _ in range(self.cfg.eval_episodes):
 				obs, done, ep_reward, t = self.env.reset(task_idx), False, 0, 0
 				while not done:
+					torch.compiler.cudagraph_mark_step_begin()
 					action = self.agent.act(obs, t0=t==0, eval_mode=True, task=task_idx)
 					obs, reward, done, info = self.env.step(action)
 					ep_reward += reward
@@ -37,20 +38,15 @@ class OfflineTrainer(Trainer):
 				f'episode_reward+{self.cfg.tasks[task_idx]}': np.nanmean(ep_rewards),
 				f'episode_success+{self.cfg.tasks[task_idx]}': np.nanmean(ep_successes),})
 		return results
-				
-	def train(self):
-		"""Train a TD-MPC2 agent."""
-		assert self.cfg.multitask and self.cfg.task in {'mt30', 'mt80'}, \
-			'Offline training only supports multitask training with mt30 or mt80 task sets.'
-
-		# Load data
-		assert self.cfg.task in self.cfg.data_dir, \
-			f'Expected data directory {self.cfg.data_dir} to contain {self.cfg.task}, ' \
-			f'please double-check your config.'
+	
+	def _load_dataset(self):
+		"""Load dataset for offline training."""
 		fp = Path(os.path.join(self.cfg.data_dir, '*.pt'))
 		fps = sorted(glob(str(fp)))
 		assert len(fps) > 0, f'No data found at {fp}'
 		print(f'Found {len(fps)} files in {fp}')
+		if len(fps) < (20 if self.cfg.task == 'mt80' else 4):
+			print(f'WARNING: expected 20 files for mt80 task set, 4 files for mt30 task set, found {len(fps)} files.')
 	
 		# Create buffer for sampling
 		_cfg = deepcopy(self.cfg)
@@ -59,14 +55,20 @@ class OfflineTrainer(Trainer):
 		_cfg.steps = _cfg.buffer_size
 		self.buffer = Buffer(_cfg)
 		for fp in tqdm(fps, desc='Loading data'):
-			td = torch.load(fp)
+			td = torch.load(fp, weights_only=False)
 			assert td.shape[1] == _cfg.episode_length, \
 				f'Expected episode length {td.shape[1]} to match config episode length {_cfg.episode_length}, ' \
 				f'please double-check your config.'
-			for i in range(len(td)):
-				self.buffer.add(td[i])
-		assert self.buffer.num_eps == self.buffer.capacity, \
-			f'Buffer has {self.buffer.num_eps} episodes, expected {self.buffer.capacity} episodes.'
+			self.buffer.load(td)
+		expected_episodes = _cfg.buffer_size // _cfg.episode_length
+		if self.buffer.num_eps != expected_episodes:
+			print(f'WARNING: buffer has {self.buffer.num_eps} episodes, expected {expected_episodes} episodes for {self.cfg.task} task set.')
+
+	def train(self):
+		"""Train a TD-MPC2 agent."""
+		assert self.cfg.multitask and self.cfg.task in {'mt30', 'mt80'}, \
+			'Offline training only supports multitask training with mt30 or mt80 task sets.'
+		self._load_dataset()
 		
 		print(f'Training agent for {self.cfg.steps} iterations...')
 		metrics = {}
diff --git a/tdmpc2/trainer/online_trainer.py b/tdmpc2/trainer/online_trainer.py
index 866f7d6..84991e7 100755
--- a/tdmpc2/trainer/online_trainer.py
+++ b/tdmpc2/trainer/online_trainer.py
@@ -3,7 +3,6 @@ from time import time
 import numpy as np
 import torch
 from tensordict.tensordict import TensorDict
-
 from trainer.base import Trainer
 
 
@@ -32,6 +31,7 @@ class OnlineTrainer(Trainer):
 			if self.cfg.save_video:
 				self.logger.video.init(self.env, enabled=(i==0))
 			while not done:
+				torch.compiler.cudagraph_mark_step_begin()
 				action = self.agent.act(obs, t0=t==0, eval_mode=True)
 				obs, reward, done, info = self.env.step(action)
 				ep_reward += reward
@@ -59,19 +59,18 @@ class OnlineTrainer(Trainer):
 			reward = torch.tensor(float('nan'))
 		if terminated is None:
 			terminated = torch.tensor(float('nan'))
-		td = TensorDict(dict(
+		td = TensorDict(
 			obs=obs,
 			action=action.unsqueeze(0),
 			reward=reward.unsqueeze(0),
 			terminated=terminated.unsqueeze(0),
-		), batch_size=(1,))
+		batch_size=(1,))
 		return td
 
 	def train(self):
 		"""Train a TD-MPC2 agent."""
-		train_metrics, done, eval_next = {}, True, True
+		train_metrics, done, eval_next = {}, True, False
 		while self._step <= self.cfg.steps:
-
 			# Evaluate agent periodically
 			if self._step % self.cfg.eval_freq == 0:
 				eval_next = True
@@ -116,5 +115,5 @@ class OnlineTrainer(Trainer):
 				train_metrics.update(_train_metrics)
 
 			self._step += 1
-	
+
 		self.logger.finish(self.agent)