Adds action clipping to rsl-rl wrapper

scripts/reinforcement_learning/rsl_rl/train.py

@@ -124,7 +124,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         env = gym.wrappers.RecordVideo(env, **video_kwargs)
 
     # wrap around environment for rsl-rl
-    env = RslRlVecEnvWrapper(env)
+    env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
 
     # create runner from rsl-rl
     runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)

source/isaaclab_rl/isaaclab_rl/rsl_rl/rl_cfg.py

@@ -98,6 +98,9 @@ class RslRlOnPolicyRunnerCfg:
     algorithm: RslRlPpoAlgorithmCfg = MISSING
     """The algorithm configuration."""
 
+    clip_actions: float | None = None
+    """The clipping value for actions. If ``None``, then no clipping is done."""
+
     ##
     # Checkpointing parameters
     ##

source/isaaclab_rl/isaaclab_rl/rsl_rl/vecenv_wrapper.py

@@ -30,15 +30,15 @@ class RslRlVecEnvWrapper(VecEnv):
         https://github.com/leggedrobotics/rsl_rl/blob/master/rsl_rl/env/vec_env.py
     """
 
-    def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv):
+    def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv, clip_actions: float | None = None):
         """Initializes the wrapper.
 
         Note:
             The wrapper calls :meth:`reset` at the start since the RSL-RL runner does not call reset.
 
         Args:
             env: The environment to wrap around.
-
+            clip_actions: The clipping value for actions. If ``None``, then no clipping is done.
         Raises:
             ValueError: When the environment is not an instance of :class:`ManagerBasedRLEnv` or :class:`DirectRLEnv`.
         """
@@ -50,10 +50,14 @@ def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv):
             )
         # initialize the wrapper
         self.env = env
+        self.clip_actions = clip_actions
+
         # store information required by wrapper
         self.num_envs = self.unwrapped.num_envs
         self.device = self.unwrapped.device
         self.max_episode_length = self.unwrapped.max_episode_length
+
+        # obtain dimensions of the environment
         if hasattr(self.unwrapped, "action_manager"):
             self.num_actions = self.unwrapped.action_manager.total_action_dim
         else:
@@ -72,6 +76,7 @@ def __init__(self, env: ManagerBasedRLEnv | DirectRLEnv):
             self.num_privileged_obs = gym.spaces.flatdim(self.unwrapped.single_observation_space["critic"])
         else:
             self.num_privileged_obs = 0
+
         # reset at the start since the RSL-RL runner does not call reset
         self.env.reset()
 
@@ -105,7 +110,10 @@ def observation_space(self) -> gym.Space:
     @property
     def action_space(self) -> gym.Space:
         """Returns the :attr:`Env` :attr:`action_space`."""
-        return self.env.action_space
+        if self.clip_actions is None:
+            return self.env.action_space
+        else:
+            return gym.spaces.Box(low=-self.clip_actions, high=self.clip_actions, shape=(self.num_actions,))
 
     @classmethod
     def class_name(cls) -> str:
@@ -160,6 +168,9 @@ def reset(self) -> tuple[torch.Tensor, dict]:  # noqa: D102
         return obs_dict["policy"], {"observations": obs_dict}
 
     def step(self, actions: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict]:
+        # clip actions
+        if self.clip_actions is not None:
+            actions = torch.clamp(actions, -self.clip_actions, self.clip_actions)
         # record step information
         obs_dict, rew, terminated, truncated, extras = self.env.step(actions)
         # compute dones for compatibility with RSL-RL