Adds checkpoint CLI argument to skrl's train script to resume training (#244)

# Description

Add `checkpoint` CLI argument to skrl's train script to resume training.
It solves https://github.com/isaac-sim/IsaacLab/issues/1635

## Type of change

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

## 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

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Co-authored-by: Kelly Guo <kellyg@nvidia.com>

scripts/reinforcement_learning/skrl/train.py

@@ -28,6 +28,7 @@ parser.add_argument("--seed", type=int, default=None, help="Seed used for the en
 parser.add_argument(
     "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
 )
+parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint to resume training.")
 parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
 parser.add_argument(
     "--ml_framework",
@@ -48,7 +49,7 @@ parser.add_argument(
 AppLauncher.add_app_launcher_args(parser)
 # parse the arguments
 args_cli, hydra_args = parser.parse_known_args()
-
+# always enable cameras to record video
 if args_cli.video:
     args_cli.enable_cameras = True
 
@@ -92,6 +93,7 @@ from isaaclab.envs import (
     ManagerBasedRLEnvCfg,
     multi_agent_to_single_agent,
 )
+from isaaclab.utils.assets import retrieve_file_path
 from isaaclab.utils.dict import print_dict
 from isaaclab.utils.io import dump_pickle, dump_yaml
 
@@ -151,6 +153,9 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
     dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)
 
+    # get checkpoint path (to resume training)
+    resume_path = retrieve_file_path(args_cli.checkpoint) if args_cli.checkpoint else None
+
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
 
@@ -177,6 +182,11 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # https://skrl.readthedocs.io/en/latest/api/utils/runner.html
     runner = Runner(env, agent_cfg)
 
+    # load checkpoint (if specified)
+    if resume_path:
+        print(f"[INFO] Loading model checkpoint from: {resume_path}")
+        runner.agent.load(resume_path)
+
     # run training
     runner.run()