Updates skrl integration to support training/evaluation using JAX (#592)

# Description

This PR updates the skrl integration to support training/evaluation
using JAX ML framework

## Type of change

- New feature (non-breaking change which adds functionality)
- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have run all the tests with `./isaaclab.sh --test` and they pass
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

docs/source/setup/sample.rst

@@ -184,14 +184,31 @@ from the environments into the respective libraries function argument and return
 -  Training an agent with
    `SKRL <https://skrl.readthedocs.io>`__ on ``Isaac-Reach-Franka-v0``:
 
-   .. code:: bash
+   .. tab-set::
 
-      # install python module (for skrl)
-      ./isaaclab.sh -i skrl
-      # run script for training
-      ./isaaclab.sh -p source/standalone/workflows/skrl/train.py --task Isaac-Reach-Franka-v0 --headless
-      # run script for playing with 32 environments
-      ./isaaclab.sh -p source/standalone/workflows/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
+      .. tab-item:: PyTorch
+
+         .. code:: bash
+
+            # install python module (for skrl)
+            ./isaaclab.sh -i skrl
+            # run script for training
+            ./isaaclab.sh -p source/standalone/workflows/skrl/train.py --task Isaac-Reach-Franka-v0 --headless
+            # run script for playing with 32 environments
+            ./isaaclab.sh -p source/standalone/workflows/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
+
+      .. tab-item:: JAX
+
+         .. code:: bash
+
+            # install python module (for skrl)
+            ./isaaclab.sh -i skrl
+            # install skrl dependencies for JAX. Visit https://skrl.readthedocs.io/en/latest/intro/installation.html for more details
+            ./isaaclab.sh -p -m pip install skrl["jax"]
+            # run script for training
+            ./isaaclab.sh -p source/standalone/workflows/skrl/train.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax
+            # run script for playing with 32 environments
+            ./isaaclab.sh -p source/standalone/workflows/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32  --ml_framework jax --checkpoint /PATH/TO/model.pt
 
 -  Training an agent with
    `RL-Games <https://github.com/Denys88/rl_games>`__ on ``Isaac-Ant-v0``:

source/extensions/omni.isaac.lab_tasks/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.7.9"
+version = "0.7.10"
 
 # Description
 title = "Isaac Lab Environments"

source/extensions/omni.isaac.lab_tasks/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.7.10 (2024-07-02)
+~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Extended skrl wrapper to support training/evaluation using JAX
+
+
 0.7.9 (2024-07-01)
 ~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/utils/wrappers/skrl.py

@@ -11,13 +11,14 @@ The following example shows how to wrap an environment for skrl:
 
     from omni.isaac.lab_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper
 
-    env = SkrlVecEnvWrapper(env)
+    env = SkrlVecEnvWrapper(env, ml_framework="torch")  # or ml_framework="jax"
 
 Or, equivalently, by directly calling the skrl library API as follows:
 
 .. code-block:: python
 
-    from skrl.envs.torch.wrappers import wrap_env
+    from skrl.envs.torch.wrappers import wrap_env  # for PyTorch, or...
+    from skrl.envs.jax.wrappers import wrap_env    # for JAX
 
     env = wrap_env(env, wrapper="isaaclab")
 
@@ -26,10 +27,7 @@ Or, equivalently, by directly calling the skrl library API as follows:
 # needed to import for type hinting: Agent | list[Agent]
 from __future__ import annotations
 
-from skrl.envs.wrappers.torch import wrap_env
-from skrl.resources.preprocessors.torch import RunningStandardScaler  # noqa: F401
-from skrl.resources.schedulers.torch import KLAdaptiveLR  # noqa: F401
-from skrl.utils.model_instantiators.torch import Shape  # noqa: F401
+from typing import Literal
 
 from omni.isaac.lab.envs import DirectRLEnv, ManagerBasedRLEnv
 
@@ -38,14 +36,18 @@ Configuration Parser.
 """
 
 
-def process_skrl_cfg(cfg: dict) -> dict:
+def process_skrl_cfg(cfg: dict, ml_framework: Literal["torch", "jax", "jax-numpy"] = "torch") -> dict:
     """Convert simple YAML types to skrl classes/components.
 
     Args:
         cfg: A configuration dictionary.
+        ml_framework: The ML framework to use for the wrapper. Defaults to "torch".
 
     Returns:
         A dictionary containing the converted configuration.
+
+    Raises:
+        ValueError: If the specified ML framework is not valid.
     """
     _direct_eval = [
         "learning_rate_scheduler",
@@ -62,6 +64,20 @@ def process_skrl_cfg(cfg: dict) -> dict:
         return reward_shaper
 
     def update_dict(d):
+        # import statements according to the ML framework
+        if ml_framework.startswith("torch"):
+            from skrl.resources.preprocessors.torch import RunningStandardScaler  # noqa: F401
+            from skrl.resources.schedulers.torch import KLAdaptiveLR  # noqa: F401
+            from skrl.utils.model_instantiators.torch import Shape  # noqa: F401
+        elif ml_framework.startswith("jax"):
+            from skrl.resources.preprocessors.jax import RunningStandardScaler  # noqa: F401
+            from skrl.resources.schedulers.jax import KLAdaptiveLR  # noqa: F401
+            from skrl.utils.model_instantiators.jax import Shape  # noqa: F401
+        else:
+            ValueError(
+                f"Invalid ML framework for skrl: {ml_framework}. Available options are: 'torch', 'jax' or 'jax-numpy'"
+            )
+
         for key, value in d.items():
             if isinstance(value, dict):
                 update_dict(value)
@@ -84,7 +100,7 @@ Vectorized environment wrapper.
 """
 
 
-def SkrlVecEnvWrapper(env: ManagerBasedRLEnv):
+def SkrlVecEnvWrapper(env: ManagerBasedRLEnv, ml_framework: Literal["torch", "jax", "jax-numpy"] = "torch"):
     """Wraps around Isaac Lab environment for skrl.
 
     This function wraps around the Isaac Lab environment. Since the :class:`ManagerBasedRLEnv` environment
@@ -94,9 +110,11 @@ def SkrlVecEnvWrapper(env: ManagerBasedRLEnv):
 
     Args:
         env: The environment to wrap around.
+        ml_framework: The ML framework to use for the wrapper. Defaults to "torch".
 
     Raises:
         ValueError: When the environment is not an instance of :class:`ManagerBasedRLEnv`.
+        ValueError: If the specified ML framework is not valid.
 
     Reference:
         https://skrl.readthedocs.io/en/latest/api/envs/wrapping.html
@@ -106,5 +124,16 @@ def SkrlVecEnvWrapper(env: ManagerBasedRLEnv):
         raise ValueError(
             f"The environment must be inherited from ManagerBasedRLEnv or DirectRLEnv. Environment type: {type(env)}"
         )
+
+    # import statements according to the ML framework
+    if ml_framework.startswith("torch"):
+        from skrl.envs.wrappers.torch import wrap_env
+    elif ml_framework.startswith("jax"):
+        from skrl.envs.wrappers.jax import wrap_env
+    else:
+        ValueError(
+            f"Invalid ML framework for skrl: {ml_framework}. Available options are: 'torch', 'jax' or 'jax-numpy'"
+        )
+
     # wrap and return the environment
     return wrap_env(env, wrapper="isaaclab")

source/standalone/workflows/skrl/play.py

@@ -26,6 +26,14 @@ parser.add_argument(
 parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
 parser.add_argument("--task", type=str, default=None, help="Name of the task.")
 parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
+parser.add_argument(
+    "--ml_framework",
+    type=str,
+    default="torch",
+    choices=["torch", "jax", "jax-numpy"],
+    help="The ML framework used for training the skrl agent.",
+)
+
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
 # parse the arguments
@@ -41,8 +49,14 @@ import gymnasium as gym
 import os
 import torch
 
-from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
-from skrl.utils.model_instantiators.torch import deterministic_model, gaussian_model, shared_model
+import skrl
+
+if args_cli.ml_framework.startswith("torch"):
+    from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
+    from skrl.utils.model_instantiators.torch import deterministic_model, gaussian_model, shared_model
+elif args_cli.ml_framework.startswith("jax"):
+    from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
+    from skrl.utils.model_instantiators.jax import deterministic_model, gaussian_model
 
 import omni.isaac.lab_tasks  # noqa: F401
 from omni.isaac.lab_tasks.utils import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg
@@ -51,7 +65,10 @@ from omni.isaac.lab_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper, process_
 
 def main():
     """Play with skrl agent."""
-    # parse env configuration
+    # configure the ML framework into the global skrl variable
+    if args_cli.ml_framework.startswith("jax"):
+        skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
+    # parse configuration
     env_cfg = parse_env_cfg(
         args_cli.task, use_gpu=not args_cli.cpu, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
     )
@@ -60,24 +77,26 @@ def main():
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg)
     # wrap around environment for skrl
-    env = SkrlVecEnvWrapper(env)  # same as: `wrap_env(env, wrapper="isaaclab")`
+    env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework)  # same as: `wrap_env(env, wrapper="isaaclab")`
 
     # instantiate models using skrl model instantiator utility
     # https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html
     models = {}
+    if args_cli.ml_framework.startswith("jax"):
+        experiment_cfg["models"]["separate"] = True  # shared model is not supported in JAX
     # non-shared models
     if experiment_cfg["models"]["separate"]:
         models["policy"] = gaussian_model(
             observation_space=env.observation_space,
             action_space=env.action_space,
             device=env.device,
-            **process_skrl_cfg(experiment_cfg["models"]["policy"]),
+            **process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
         )
         models["value"] = deterministic_model(
             observation_space=env.observation_space,
             action_space=env.action_space,
             device=env.device,
-            **process_skrl_cfg(experiment_cfg["models"]["value"]),
+            **process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
         )
     # shared models
     else:
@@ -88,17 +107,21 @@ def main():
             structure=None,
             roles=["policy", "value"],
             parameters=[
-                process_skrl_cfg(experiment_cfg["models"]["policy"]),
-                process_skrl_cfg(experiment_cfg["models"]["value"]),
+                process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
+                process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
             ],
         )
         models["value"] = models["policy"]
+    # instantiate models' state dict
+    if args_cli.ml_framework.startswith("jax"):
+        for role, model in models.items():
+            model.init_state_dict(role)
 
     # configure and instantiate PPO agent
     # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html
     agent_cfg = PPO_DEFAULT_CONFIG.copy()
     experiment_cfg["agent"]["rewards_shaper"] = None  # avoid 'dictionary changed size during iteration'
-    agent_cfg.update(process_skrl_cfg(experiment_cfg["agent"]))
+    agent_cfg.update(process_skrl_cfg(experiment_cfg["agent"], ml_framework=args_cli.ml_framework))
 
     agent_cfg["state_preprocessor_kwargs"].update({"size": env.observation_space, "device": env.device})
     agent_cfg["value_preprocessor_kwargs"].update({"size": 1, "device": env.device})

source/standalone/workflows/skrl/train.py

@@ -33,6 +33,13 @@ parser.add_argument(
     "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
 )
 parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument(
+    "--ml_framework",
+    type=str,
+    default="torch",
+    choices=["torch", "jax", "jax-numpy"],
+    help="The ML framework used for training the skrl agent.",
+)
 
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
@@ -52,11 +59,19 @@ import gymnasium as gym
 import os
 from datetime import datetime
 
-from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
-from skrl.memories.torch import RandomMemory
-from skrl.trainers.torch import SequentialTrainer
+import skrl
 from skrl.utils import set_seed
-from skrl.utils.model_instantiators.torch import deterministic_model, gaussian_model, shared_model
+
+if args_cli.ml_framework.startswith("torch"):
+    from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
+    from skrl.memories.torch import RandomMemory
+    from skrl.trainers.torch import SequentialTrainer
+    from skrl.utils.model_instantiators.torch import deterministic_model, gaussian_model, shared_model
+elif args_cli.ml_framework.startswith("jax"):
+    from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
+    from skrl.memories.jax import RandomMemory
+    from skrl.trainers.jax import SequentialTrainer
+    from skrl.utils.model_instantiators.jax import deterministic_model, gaussian_model
 
 from omni.isaac.lab.utils.dict import print_dict
 from omni.isaac.lab.utils.io import dump_pickle, dump_yaml
@@ -68,6 +83,10 @@ from omni.isaac.lab_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper, process_
 
 def main():
     """Train with skrl agent."""
+    # configure the ML framework into the global skrl variable
+    if args_cli.ml_framework.startswith("jax"):
+        skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
+
     # read the seed from command line
     args_cli_seed = args_cli.seed
 
@@ -93,6 +112,8 @@ def main():
 
     # multi-gpu training config
     if args_cli.distributed:
+        if args_cli.ml_framework.startswith("jax"):
+            raise ValueError("Multi-GPU distributed training not yet supported in JAX")
         # update env config device
         env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
 
@@ -120,7 +141,7 @@ def main():
         print_dict(video_kwargs, nesting=4)
         env = gym.wrappers.RecordVideo(env, **video_kwargs)
     # wrap around environment for skrl
-    env = SkrlVecEnvWrapper(env)  # same as: `wrap_env(env, wrapper="isaaclab")`
+    env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework)  # same as: `wrap_env(env, wrapper="isaaclab")`
 
     # set seed for the experiment (override from command line)
     set_seed(args_cli_seed if args_cli_seed is not None else experiment_cfg["seed"])
@@ -128,19 +149,21 @@ def main():
     # instantiate models using skrl model instantiator utility
     # https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html
     models = {}
+    if args_cli.ml_framework.startswith("jax"):
+        experiment_cfg["models"]["separate"] = True  # shared model is not supported in JAX
     # non-shared models
     if experiment_cfg["models"]["separate"]:
         models["policy"] = gaussian_model(
             observation_space=env.observation_space,
             action_space=env.action_space,
             device=env.device,
-            **process_skrl_cfg(experiment_cfg["models"]["policy"]),
+            **process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
         )
         models["value"] = deterministic_model(
             observation_space=env.observation_space,
             action_space=env.action_space,
             device=env.device,
-            **process_skrl_cfg(experiment_cfg["models"]["value"]),
+            **process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
         )
     # shared models
     else:
@@ -151,11 +174,15 @@ def main():
             structure=None,
             roles=["policy", "value"],
             parameters=[
-                process_skrl_cfg(experiment_cfg["models"]["policy"]),
-                process_skrl_cfg(experiment_cfg["models"]["value"]),
+                process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
+                process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
             ],
         )
         models["value"] = models["policy"]
+    # instantiate models' state dict
+    if args_cli.ml_framework.startswith("jax"):
+        for role, model in models.items():
+            model.init_state_dict(role)
 
     # instantiate a RandomMemory as rollout buffer (any memory can be used for this)
     # https://skrl.readthedocs.io/en/latest/api/memories/random.html
@@ -166,7 +193,7 @@ def main():
     # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html
     agent_cfg = PPO_DEFAULT_CONFIG.copy()
     experiment_cfg["agent"]["rewards_shaper"] = None  # avoid 'dictionary changed size during iteration'
-    agent_cfg.update(process_skrl_cfg(experiment_cfg["agent"]))
+    agent_cfg.update(process_skrl_cfg(experiment_cfg["agent"], ml_framework=args_cli.ml_framework))
 
     agent_cfg["state_preprocessor_kwargs"].update({"size": env.observation_space, "device": env.device})
     agent_cfg["value_preprocessor_kwargs"].update({"size": 1, "device": env.device})