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Nicklas Hansen
2024-01-21 21:21:44 -08:00
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README.md
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Official implementation of
[TD-MPC2: Scalable, Robust World Models for Continuous Control](https://nicklashansen.github.io/td-mpc2) by
[TD-MPC2: Scalable, Robust World Models for Continuous Control](https://www.tdmpc2.com) by
[Nicklas Hansen](https://nicklashansen.github.io/), [Hao Su](https://cseweb.ucsd.edu/~haosu/)\*, [Xiaolong Wang](https://xiaolonw.github.io/)\* (UC San Diego)</br>
[Nicklas Hansen](https://nicklashansen.github.io), [Hao Su](https://cseweb.ucsd.edu/~haosu)\*, [Xiaolong Wang](https://xiaolonw.github.io)\* (UC San Diego)</br>
<img src="assets/0.gif" width="12.5%"><img src="assets/1.gif" width="12.5%"><img src="assets/2.gif" width="12.5%"><img src="assets/3.gif" width="12.5%"><img src="assets/4.gif" width="12.5%"><img src="assets/5.gif" width="12.5%"><img src="assets/6.gif" width="12.5%"><img src="assets/7.gif" width="12.5%"></br>
[[Website]](https://nicklashansen.github.io/td-mpc2) [[Paper]](https://arxiv.org/abs/2310.16828) [[Models]](https://nicklashansen.github.io/td-mpc2/models) [[Dataset]](https://nicklashansen.github.io/td-mpc2/dataset)
[[Website]](https://www.tdmpc2.com) [[Paper]](https://arxiv.org/abs/2310.16828) [[Models]](https://www.tdmpc2.com/models) [[Dataset]](https://www.tdmpc2.com/dataset)
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@@ -18,7 +18,7 @@ TD-MPC**2** is a scalable, robust model-based reinforcement learning algorithm.
<img src="assets/8.png" width="100%" style="max-width: 640px"><br/>
This repository contains code for training and evaluating both single-task online RL and multi-task offline RL TD-MPC**2** agents. We additionally open-source **300+** [model checkpoints](https://nicklashansen.github.io/td-mpc2/models) (including 12 multi-task models) across 4 task domains: [DMControl](https://arxiv.org/abs/1801.00690), [Meta-World](https://meta-world.github.io/), [ManiSkill2](https://maniskill2.github.io/), and [MyoSuite](https://sites.google.com/view/myosuite), as well as our [30-task and 80-task datasets](https://nicklashansen.github.io/td-mpc2/dataset) used to train the multi-task models. Our codebase supports both state and pixel observations. We hope that this repository will serve as a useful community resource for future research on model-based RL.
This repository contains code for training and evaluating both single-task online RL and multi-task offline RL TD-MPC**2** agents. We additionally open-source **300+** [model checkpoints](https://www.tdmpc2.com/models) (including 12 multi-task models) across 4 task domains: [DMControl](https://arxiv.org/abs/1801.00690), [Meta-World](https://meta-world.github.io/), [ManiSkill2](https://maniskill2.github.io/), and [MyoSuite](https://sites.google.com/view/myosuite), as well as our [30-task and 80-task datasets](https://www.tdmpc2.com/dataset) used to train the multi-task models. Our codebase supports both state and pixel observations. We hope that this repository will serve as a useful community resource for future research on model-based RL.
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@@ -29,17 +29,19 @@ You will need a machine with a GPU and at least 12 GB of RAM for single-task onl
We provide a `Dockerfile` for easy installation. You can build the docker image by running
```
cd docker && docker build . -t <user>/tdmpc2:0.1.0
cd docker && docker build . -t <user>/tdmpc2:1.0.0
```
If you prefer to install dependencies manually, start by installing dependencies via `conda` by running one of the following commands:
This docker image contains all dependencies needed for running DMControl, Meta-World, and ManiSkill2 experiments.
If you prefer to install dependencies manually, start by installing dependencies via `conda` by running the following command:
```
conda env create -f docker/environment.yaml
conda env create -f docker/environment_minimal.yaml
pip install gym==0.21.0
```
The `environment.yaml` file installs dependencies required for all environments, whereas `environment_minimal.yaml` only installs dependencies for training on DMControl tasks.
The `environment.yaml` file installs dependencies required for training on DMControl tasks. Other domains can be installed by following the instructions in `environment.yaml`.
If you want to run ManiSkill2, you will additionally need to download and link the necessary assets by running