Supports rl games wrapper with dictionary observation (#3340)

# Description

This PR opens the possibility to use dictionary observation for rl-games
application.
This benefits:
1. combination of high + low dim observations percolate into actor and
critic in rl-games
2. avoid double computation if actor and critic share the same
observation


## Type of change

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applicable. -->

- New feature (non-breaking change which adds functionality)

## Screenshots

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## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] 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

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scripts/reinforcement_learning/rl_games/play.py

@@ -134,6 +134,8 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     rl_device = agent_cfg["params"]["config"]["device"]
     clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf)
     clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf)
+    obs_groups = agent_cfg["params"]["env"].get("obs_groups")
+    concate_obs_groups = agent_cfg["params"]["env"].get("concate_obs_groups", True)
 
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
@@ -155,7 +157,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         env = gym.wrappers.RecordVideo(env, **video_kwargs)
 
     # wrap around environment for rl-games
-    env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions)
+    env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions, obs_groups, concate_obs_groups)
 
     # register the environment to rl-games registry
     # note: in agents configuration: environment name must be "rlgpu"

scripts/reinforcement_learning/rl_games/train.py

@@ -148,6 +148,8 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     rl_device = agent_cfg["params"]["config"]["device"]
     clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf)
     clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf)
+    obs_groups = agent_cfg["params"]["env"].get("obs_groups")
+    concate_obs_groups = agent_cfg["params"]["env"].get("concate_obs_groups", True)
 
     # set the IO descriptors output directory if requested
     if isinstance(env_cfg, ManagerBasedRLEnvCfg):
@@ -178,7 +180,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         env = gym.wrappers.RecordVideo(env, **video_kwargs)
 
     # wrap around environment for rl-games
-    env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions)
+    env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions, obs_groups, concate_obs_groups)
 
     # register the environment to rl-games registry
     # note: in agents configuration: environment name must be "rlgpu"

source/isaaclab_rl/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.2.4"
+version = "0.3.0"
 
 # Description
 title = "Isaac Lab RL"

source/isaaclab_rl/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.3.0 (2025-09-03)
+~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Enhanced rl-games wrapper to allow dict observation.
+
+
 0.2.4 (2025-08-07)
 ~~~~~~~~~~~~~~~~~~