Fixes setting the device from CLI in the RL training scripts (#1013)

# Description

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 #1012 

## Type of change

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

## Screenshots

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.

## 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
- [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/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

@@ -120,7 +120,8 @@ 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 = args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
-
+    env_cfg.sim.device = args_cli.device
+
     # specify directory for logging experiments
     log_root_path = os.path.join("logs", "skrl", agent_cfg["agent"]["experiment"]["directory"])
     log_root_path = os.path.abspath(log_root_path)