Add support for special VecEnv (brax, IsaacSim, ...)

.github/workflows/ci.yml

@@ -14,6 +14,7 @@ jobs:
     env:
       TERM: xterm-256color
       FORCE_COLOR: 1
+      HF_TOKEN: ${{ secrets.HF_TOKEN }}
     # Skip CI if [ci skip] in the commit message
     if: "! contains(toJSON(github.event.commits.*.message), '[ci skip]')"
     runs-on: ubuntu-latest

.github/workflows/trained_agents.yml

@@ -14,7 +14,7 @@ jobs:
     env:
       TERM: xterm-256color
       FORCE_COLOR: 1
-
+      HF_TOKEN: ${{ secrets.HF_TOKEN }}
     # Skip CI if [ci skip] in the commit message
     if: "! contains(toJSON(github.event.commits.*.message), '[ci skip]')"
     runs-on: ubuntu-latest

CHANGELOG.md

@@ -1,3 +1,21 @@
+## Release 2.6.0a2 (WIP)
+
+### Breaking Changes
+- Upgraded to SB3 >= 2.6.0
+
+### New Features
+- Save the exact command line used to launch a training
+- Added support for special vectorized env (e.g. Brax, IsaacSim) by allowing to override the `VecEnv` class use to instantiate the env in the `ExperimentManager`
+- Allow to disable auto-logging by passing `--log-interval -2` (useful when logging things manually)
+- Added Gymnasium v1.1 support
+
+### Bug fixes
+- Fixed use of old HF api in `get_hf_trained_models()`
+
+### Documentation
+
+### Other
+
 ## Release 2.5.0 (2025-01-27)
 
 ### Breaking Changes

requirements.txt

@@ -1,7 +1,7 @@
 gym==0.26.2
-stable-baselines3[extra,tests,docs]>=2.5.0,<3.0
+stable-baselines3[extra,tests,docs]>=2.6.0a2,<3.0
 box2d-py==2.3.8
-pybullet_envs_gymnasium>=0.5.0
+pybullet_envs_gymnasium>=0.6.0
 # minigrid
 cloudpickle>=2.2.1
 # optuna plots:

rl_zoo3/enjoy.py

@@ -162,6 +162,7 @@ def enjoy() -> None:  # noqa: C901
         should_render=not args.no_render,
         hyperparams=hyperparams,
         env_kwargs=env_kwargs,
+        vec_env_cls=ExperimentManager.default_vec_env_cls,
     )
 
     kwargs = dict(seed=args.seed)

rl_zoo3/exp_manager.py

@@ -2,6 +2,7 @@
 import importlib
 import os
 import pickle as pkl
+import sys
 import time
 import warnings
 from collections import OrderedDict
@@ -60,6 +61,9 @@ class ExperimentManager:
     Please take a look at `train.py` to have the details for each argument.
     """
 
+    # For special VecEnv like Brax, IsaacLab, ...
+    default_vec_env_cls: Optional[type[VecEnv]] = None
+
     def __init__(
         self,
         args: argparse.Namespace,
@@ -122,6 +126,10 @@ def __init__(
         self.optimization_log_path = optimization_log_path
 
         self.vec_env_class = {"dummy": DummyVecEnv, "subproc": SubprocVecEnv}[vec_env_type]
+        # Override
+        if self.default_vec_env_cls is not None:
+            self.vec_env_class = self.default_vec_env_cls
+
         self.vec_env_wrapper: Optional[Callable] = None
 
         self.vec_env_kwargs: dict[str, Any] = {}
@@ -224,8 +232,13 @@ def learn(self, model: BaseAlgorithm) -> None:
         :param model: an initialized RL model
         """
         kwargs: dict[str, Any] = {}
+        # log_interval == -1 -> default
+        # < -2 -> no auto-logging
         if self.log_interval > -1:
             kwargs = {"log_interval": self.log_interval}
+        elif self.log_interval < -1:
+            # Deactivate auto-logging, helpful when using callback like LogEveryNTimesteps
+            kwargs = {"log_interval": None}
 
         if len(self.callbacks) > 0:
             kwargs["callback"] = self.callbacks
@@ -288,6 +301,13 @@ def _save_config(self, saved_hyperparams: dict[str, Any]) -> None:
             ordered_args = OrderedDict([(key, vars(self.args)[key]) for key in sorted(vars(self.args).keys())])
             yaml.dump(ordered_args, f)
 
+        # Save command used to train
+        command = "python3 " + " ".join(sys.argv)
+        # Python 3.10+
+        if hasattr(sys, "orig_argv"):
+            command = " ".join(sys.orig_argv)
+        (Path(self.params_path) / "command.txt").write_text(command)
+
         print(f"Log path: {self.save_path}")
 
     def read_hyperparameters(self) -> tuple[dict[str, Any], dict[str, Any]]:

rl_zoo3/train.py

@@ -32,7 +32,12 @@ def train() -> None:
     )
     parser.add_argument("-n", "--n-timesteps", help="Overwrite the number of timesteps", default=-1, type=int)
     parser.add_argument("--num-threads", help="Number of threads for PyTorch (-1 to use default)", default=-1, type=int)
-    parser.add_argument("--log-interval", help="Override log interval (default: -1, no change)", default=-1, type=int)
+    parser.add_argument(
+        "--log-interval",
+        help="Override log interval (default: -1, no change, -2: no logging useful when using custom logging freq)",
+        default=-1,
+        type=int,
+    )
     parser.add_argument(
         "--eval-freq",
         help="Evaluate the agent every n steps (if negative, no evaluation). "

rl_zoo3/utils.py

@@ -208,6 +208,8 @@ def create_test_env(
     should_render: bool = True,
     hyperparams: Optional[dict[str, Any]] = None,
     env_kwargs: Optional[dict[str, Any]] = None,
+    vec_env_cls: Optional[type[VecEnv]] = None,
+    vec_env_kwargs: Optional[dict[str, Any]] = None,
 ) -> VecEnv:
     """
     Create environment for testing a trained agent
@@ -220,6 +222,8 @@ def create_test_env(
     :param should_render: For Pybullet env, display the GUI
     :param hyperparams: Additional hyperparams (ex: n_stack)
     :param env_kwargs: Optional keyword argument to pass to the env constructor
+    :param vec_env_cls: ``VecEnv`` class constructor.
+    :param vec_env_kwargs: Keyword arguments to pass to the ``VecEnv`` class constructor.
     :return:
     """
     # Create the environment and wrap it if necessary
@@ -231,9 +235,9 @@ def create_test_env(
     if "env_wrapper" in hyperparams.keys():
         del hyperparams["env_wrapper"]
 
-    vec_env_kwargs: dict[str, Any] = {}
     # Avoid potential shared memory issue
-    vec_env_cls = SubprocVecEnv if n_envs > 1 else DummyVecEnv
+    if vec_env_cls is None:
+        vec_env_cls = SubprocVecEnv if n_envs > 1 else DummyVecEnv
 
     # Fix for gym 0.26, to keep old behavior
     env_kwargs = env_kwargs or {}
@@ -349,21 +353,24 @@ def get_hf_trained_models(organization: str = "sb3", check_filename: bool = Fals
     for model in models:
         # Try to extract algorithm and environment id from model card
         try:
-            env_id = model.cardData["model-index"][0]["results"][0]["dataset"]["name"]
-            algo = model.cardData["model-index"][0]["name"].lower()
+            assert model.card_data is not None
+            env_id = model.card_data["model-index"][0]["results"][0]["dataset"]["name"]
+            algo = model.card_data["model-index"][0]["name"].lower()
             # RecurrentPPO alias is "ppo_lstm" in the rl zoo
             if algo == "recurrentppo":
                 algo = "ppo_lstm"
-        except (KeyError, IndexError):
-            print(f"Skipping {model.modelId}")
+        except (KeyError, IndexError, AssertionError):
+            print(f"Skipping {model.id}")
             continue  # skip model if name env id or algo name could not be found
 
         env_name = EnvironmentName(env_id)
         model_name = ModelName(algo, env_name)
 
         # check if there is a model file in the repo
-        if check_filename and not any(f.rfilename == model_name.filename for f in api.model_info(model.modelId).siblings):
-            continue  # skip model if the repo contains no properly named model file
+        if check_filename:
+            maybe_siblings = api.model_info(model.id).siblings
+            if maybe_siblings and not any(f.rfilename == model_name.filename for f in maybe_siblings):
+                continue  # skip model if the repo contains no properly named model file
 
         trained_models[model_name] = (algo, env_id)
 
@@ -422,6 +429,8 @@ def get_saved_hyperparams(
                 normalize_kwargs = eval(hyperparams["normalize"])
                 if test_mode:
                     normalize_kwargs["norm_reward"] = norm_reward
+            elif isinstance(hyperparams["normalize"], dict):
+                normalize_kwargs = hyperparams["normalize"]
             else:
                 normalize_kwargs = {"norm_obs": hyperparams["normalize"], "norm_reward": norm_reward}
             hyperparams["normalize_kwargs"] = normalize_kwargs

rl_zoo3/version.txt

@@ -1 +1 @@
-2.5.0
+2.6.0a2

setup.py

@@ -15,8 +15,8 @@
 See https://github.com/DLR-RM/rl-baselines3-zoo
 """
 install_requires = [
-    "sb3_contrib>=2.5.0,<3.0",
-    "gymnasium>=0.29.1,<1.1.0",
+    "sb3_contrib>=2.6.0a2,<3.0",
+    "gymnasium>=0.29.1,<1.2.0",
     "huggingface_sb3>=3.0,<4.0",
     "tqdm",
     "rich",