allow missing env dependencies + update readme

README.md

@@ -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. 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://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.
 
 ----
 
@@ -32,12 +32,15 @@ We provide a `Dockerfile` for easy installation. You can build the docker image
 cd docker && docker build . -t <user>/tdmpc2:0.1.0
 ```
 
-If you prefer to install dependencies manually, start by installing dependencies via `conda` by running
+If you prefer to install dependencies manually, start by installing dependencies via `conda` by running one of the following commands:
 
 ```
 conda env create -f docker/environment.yaml
+conda env create -f docker/environment_minimal.yaml
 ```
 
+The `environment.yaml` file installs dependencies required for all environments, whereas `environment_minimal.yaml` only installs dependencies for training on DMControl tasks.
+
 If you want to run ManiSkill2, you will additionally need to download and link the necessary assets by running
 
 ```
@@ -72,11 +75,13 @@ This codebase currently supports **104** continuous control tasks from **DMContr
 | metaworld | mw-pick-place-wall
 | maniskill | pick-cube
 | maniskill | pick-ycb
-| myosuite  | myo-hand-key-turn
-| myosuite  | myo-hand-key-turn-hard
+| myosuite  | myo-key-turn
+| myosuite  | myo-key-turn-hard
 
 which can be run by specifying the `task` argument for `evaluation.py`. Multi-task training and evaluation is specified by setting `task=mt80` or `task=mt30` for the 80-task and 30-task sets, respectively.
 
+**As of Dec 27, 2023 the TD-MPC2 codebase also supports pixel observations for DMControl tasks**; use argument `obs=rgb` if you wish to train visual policies.
+
 
 ## Example usage
 
@@ -102,6 +107,7 @@ See below examples on how to train TD-MPC**2** on a single task (online RL) and
 $ python train.py task=mt80 model_size=48 batch_size=1024
 $ python train.py task=mt30 model_size=317 batch_size=1024
 $ python train.py task=dog-run steps=7000000
+$ python train.py task=walker-walk obs=rgb
 ```
 
 We recommend using default hyperparameters for single-task online RL, including the default model size of 5M parameters (`model_size=5`). Multi-task offline RL benefits from a larger model size, but larger models are also increasingly costly to train and evaluate. Available arguments are `model_size={1, 5, 19, 48, 317}`. See `config.yaml` for a full list of arguments.

docker/environment.yaml

@@ -26,6 +26,7 @@ dependencies:
     - hydra-core
     - hydra-submitit-launcher
     - submitit
+    - pandas
     - patchelf
     - protobuf
     - tqdm

docker/environment_minimal.yaml

@@ -0,0 +1,39 @@
+name: tdmpc2
+channels:
+  - pytorch-nightly
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.9.0
+  - pytorch
+  - torchvision
+  - cudatoolkit=11.7
+  - glew
+  - glib
+  - pip==21
+  - pip:
+    - absl-py
+    - glfw
+    - kornia
+    - termcolor
+    - gym==0.21.0
+    - moviepy
+    - ffmpeg
+    - imageio
+    - imageio-ffmpeg
+    - omegaconf
+    - hydra-core
+    - hydra-submitit-launcher
+    - submitit
+    - pandas
+    - patchelf
+    - protobuf
+    - tqdm
+    - setuptools==65.5.0
+    - "cython<3"
+    - dm-control
+    - pillow
+    - tensordict-nightly
+    - torchrl-nightly
+    - wandb

tdmpc2/envs/__init__.py

@@ -6,11 +6,27 @@ import 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
-# from envs.maniskill import make_env as make_maniskill_env
-# from envs.metaworld import make_env as make_metaworld_env
-# from envs.myosuite import make_env as make_myosuite_env
-from envs.exceptions import UnknownTaskError
+except:
+	make_dm_control_env = missing_dependencies
+try:
+	from envs.maniskill import make_env as make_maniskill_env
+except:
+	make_maniskill_env = missing_dependencies
+try:
+	from envs.metaworld import make_env as make_metaworld_env
+except:
+	make_metaworld_env = missing_dependencies
+try:
+	from envs.myosuite import make_env as make_myosuite_env
+except:
+	make_myosuite_env = missing_dependencies
+
 
 warnings.filterwarnings('ignore', category=DeprecationWarning)
 
@@ -27,7 +43,7 @@ def make_multitask_env(cfg):
 		_cfg.multitask = False
 		env = make_env(_cfg)
 		if env is None:
-			raise UnknownTaskError(task)
+			raise ValueError('Unknown task:', task)
 		envs.append(env)
 	env = MultitaskWrapper(cfg, envs)
 	cfg.obs_shapes = env._obs_dims
@@ -43,15 +59,16 @@ def make_env(cfg):
 	gym.logger.set_level(40)
 	if cfg.multitask:
 		env = make_multitask_env(cfg)
+
 	else:
 		env = None
-		for fn in [make_dm_control_env]: #, make_maniskill_env, make_metaworld_env, make_myosuite_env]:
+		for fn in [make_dm_control_env, make_maniskill_env, make_metaworld_env, make_myosuite_env]:
 			try:
 				env = fn(cfg)
-			except UnknownTaskError:
+			except ValueError:
 				pass
 		if env is None:
-			raise UnknownTaskError(cfg.task)
+			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)

tdmpc2/envs/dmcontrol.py

@@ -8,7 +8,6 @@ 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
-from envs.exceptions import UnknownTaskError
 import gym
 
 
@@ -187,7 +186,8 @@ def make_env(cfg):
 	domain, task = cfg.task.replace('-', '_').split('_', 1)
 	domain = dict(cup='ball_in_cup', pointmass='point_mass').get(domain, domain)
 	if (domain, task) not in suite.ALL_TASKS:
-		raise UnknownTaskError(cfg.task)
+		raise ValueError('Unknown task:', task)
+	assert cfg.obs in {'state', 'rgb'}, 'This task only supports state and rgb observations.'
 	env = suite.load(domain,
 					 task,
 					 task_kwargs={'random': cfg.seed},

tdmpc2/envs/exceptions.py

@@ -1,4 +0,0 @@
-
-class UnknownTaskError(Exception):
-	def __init__(self, task):
-		super().__init__(f'Unknown task: {task}')

tdmpc2/envs/maniskill.py

@@ -1,7 +1,6 @@
 import gym
 import numpy as np
 from envs.wrappers.time_limit import TimeLimit
-from envs.exceptions import UnknownTaskError
 
 import mani_skill2.envs
 
@@ -65,7 +64,8 @@ def make_env(cfg):
 	Make ManiSkill2 environment.
 	"""
 	if cfg.task not in MANISKILL_TASKS:
-		raise UnknownTaskError(cfg.task)
+		raise ValueError('Unknown task:', cfg.task)
+	assert cfg.obs == 'state', 'This task only supports state observations.'
 	task_cfg = MANISKILL_TASKS[cfg.task]
 	env = gym.make(
 		task_cfg['env'],

tdmpc2/envs/metaworld.py

@@ -1,7 +1,6 @@
 import numpy as np
 import gym
 from envs.wrappers.time_limit import TimeLimit
-from envs.exceptions import UnknownTaskError
 
 from metaworld.envs import ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE
 
@@ -44,7 +43,8 @@ def make_env(cfg):
 	"""
 	env_id = cfg.task.split("-", 1)[-1] + "-v2-goal-observable"
 	if not cfg.task.startswith('mw-') or env_id not in ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE:
-		raise UnknownTaskError(cfg.task)
+		raise ValueError('Unknown task:', cfg.task)
+	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)

tdmpc2/envs/myosuite.py

@@ -1,24 +1,19 @@
 import numpy as np
 import gym
 from envs.wrappers.time_limit import TimeLimit
-from envs.exceptions import UnknownTaskError
 
 
 MYOSUITE_TASKS = {
-	'myo-finger-reach': 'myoFingerReachFixed-v0',
-	'myo-finger-reach-hard': 'myoFingerReachRandom-v0',
-	'myo-finger-pose': 'myoFingerPoseFixed-v0',
-	'myo-finger-pose-hard': 'myoFingerPoseRandom-v0',
-	'myo-hand-reach': 'myoHandReachFixed-v0',
-	'myo-hand-reach-hard': 'myoHandReachRandom-v0',
-	'myo-hand-pose': 'myoHandPoseFixed-v0',
-	'myo-hand-pose-hard': 'myoHandPoseRandom-v0',
-	'myo-hand-obj-hold': 'myoHandObjHoldFixed-v0',
-	'myo-hand-obj-hold-hard': 'myoHandObjHoldRandom-v0',
-	'myo-hand-key-turn': 'myoHandKeyTurnFixed-v0',
-	'myo-hand-key-turn-hard': 'myoHandKeyTurnRandom-v0',
-	'myo-hand-pen-twirl': 'myoHandPenTwirlFixed-v0',
-	'myo-hand-pen-twirl-hard': 'myoHandPenTwirlRandom-v0',
+	'myo-reach': 'myoHandReachFixed-v0',
+	'myo-reach-hard': 'myoHandReachRandom-v0',
+	'myo-pose': 'myoHandPoseFixed-v0',
+	'myo-pose-hard': 'myoHandPoseRandom-v0',
+	'myo-obj-hold': 'myoHandObjHoldFixed-v0',
+	'myo-obj-hold-hard': 'myoHandObjHoldRandom-v0',
+	'myo-key-turn': 'myoHandKeyTurnFixed-v0',
+	'myo-key-turn-hard': 'myoHandKeyTurnRandom-v0',
+	'myo-pen-twirl': 'myoHandPenTwirlFixed-v0',
+	'myo-pen-twirl-hard': 'myoHandPenTwirlRandom-v0',
 }
 
 
@@ -50,7 +45,8 @@ def make_env(cfg):
 	Make Myosuite environment.
 	"""
 	if not cfg.task in MYOSUITE_TASKS:
-		raise UnknownTaskError(cfg.task)
+		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])
 	env = MyoSuiteWrapper(env, cfg)

tdmpc2/trainer/online_trainer.py

@@ -54,7 +54,7 @@ class OnlineTrainer(Trainer):
 		else:
 			obs = obs.unsqueeze(0).cpu()
 		if action is None:
-			action = torch.empty_like(self.env.rand_act())
+			action = torch.full_like(self.env.rand_act(), float('nan'))
 		if reward is None:
 			reward = torch.tensor(float('nan'))
 		td = TensorDict(dict(