Fixes setting the device from CLI in the RL training scripts (isaac-s…

…im#1013)

This pull request fixes the issue where the device (`CPU` or `CUDA`) is
not set correctly when using the `--device` argument in Hydra-configured
scripts like `rsl_rl/train.py` and `skrl/train.py`. The bug caused the
scripts to always default to `cuda:0`, even when `cpu` or a specific
CUDA device (e.g., `cuda:1`) was selected.

The fix adds the following line to ensure that the selected device is
properly set in `env_cfg` before initializing the environment with
`gym.make()`:

```python
env_cfg.sim.device = args_cli.device
```

Fixes isaac-sim#1012

- Bug fix (non-breaking change which fixes an issue)

Before:
- skrl/train, when running the script with --device cpu, it defaults to
cuda:0.
- rsl_rl/train.py, the script freezes at `[INFO]: Starting the
simulation. This may take a few seconds. Please wait....`

After:
- Both scripts run correctly on the specified device (e.g., cpu or
cuda:1) without defaulting to cuda:0 or freezing.

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] 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 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

CONTRIBUTORS.md

@@ -31,9 +31,10 @@ Guidelines for modifications:
 
 ## Contributors
 
-* Anton Bjørndahl Mortensen
 * Alice Zhou
+* Amr Mousa
 * Andrej Orsula
+* Anton Bjørndahl Mortensen
 * Antonio Serrano-Muñoz
 * Arjun Bhardwaj
 * Brayden Zhang

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_marl_env.py

@@ -79,6 +79,12 @@ def __init__(self, cfg: DirectMARLEnvCfg, render_mode: str | None = None, **kwar
         # initialize internal variables
         self._is_closed = False
 
+        # set the seed for the environment
+        if self.cfg.seed is not None:
+            self.seed(self.cfg.seed)
+        else:
+            carb.log_warn("Seed not set for the environment. The environment creation may not be deterministic.")
+
         # create a simulation context to control the simulator
         if SimulationContext.instance() is None:
             self.sim: SimulationContext = SimulationContext(self.cfg.sim)
@@ -88,6 +94,7 @@ def __init__(self, cfg: DirectMARLEnvCfg, render_mode: str | None = None, **kwar
         # print useful information
         print("[INFO]: Base environment:")
         print(f"\tEnvironment device    : {self.device}")
+        print(f"\tEnvironment seed      : {self.cfg.seed}")
         print(f"\tPhysics step-size     : {self.physics_dt}")
         print(f"\tRendering step-size   : {self.physics_dt * self.cfg.sim.render_interval}")
         print(f"\tEnvironment step-size : {self.step_dt}")

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_marl_env_cfg.py

@@ -41,6 +41,14 @@ class DirectMARLEnvCfg:
     """
 
     # general settings
+    seed: int | None = None
+    """The seed for the random number generator. Defaults to None, in which case the seed is not set.
+
+    Note:
+      The seed is set at the beginning of the environment initialization. This ensures that the environment
+      creation is deterministic and behaves similarly across different runs.
+    """
+
     decimation: int = MISSING
     """Number of control action updates @ sim dt per policy dt.
 

source/standalone/workflows/rl_games/train.py

@@ -74,6 +74,8 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     """Train with RL-Games agent."""
     # override configurations with non-hydra CLI arguments
     env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
+
     agent_cfg["params"]["seed"] = args_cli.seed if args_cli.seed is not None else agent_cfg["params"]["seed"]
     agent_cfg["params"]["config"]["max_epochs"] = (
         args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg["params"]["config"]["max_epochs"]

source/standalone/workflows/rsl_rl/train.py

@@ -85,6 +85,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # set the environment seed
     # note: certain randomizations occur in the environment initialization so we set the seed here
     env_cfg.seed = agent_cfg.seed
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
 
     # specify directory for logging experiments
     log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)

source/standalone/workflows/sb3/train.py

@@ -81,6 +81,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # set the environment seed
     # note: certain randomizations occur in the environment initialization so we set the seed here
     env_cfg.seed = agent_cfg["seed"]
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
 
     # directory for logging into
     log_dir = os.path.join("logs", "sb3", args_cli.task, datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))

source/standalone/workflows/skrl/train.py

@@ -106,6 +106,8 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     """Train with skrl agent."""
     # override configurations with non-hydra CLI arguments
     env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
+
     # multi-gpu training config
     if args_cli.distributed:
         env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
@@ -118,7 +120,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
 
     # set the environment seed
-    # note: certain randomizations occur in the environment initialization so we set the seed here
+    # note: certain randomization occur in the environment initialization so we set the seed here
     env_cfg.seed = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
 
     # specify directory for logging experiments
@@ -135,11 +137,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # update log_dir
     log_dir = os.path.join(log_root_path, log_dir)
 
-    # multi-gpu training config
-    if args_cli.distributed:
-        # update env config device
-        env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
-
     # dump the configuration into log-directory
     dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
     dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)