Fixes the checkpoint loading error in RSL-RL training script (#1210)

# Description

An error of `No checkpoints in the directory` will throw when resume
from a previous training with `--video` set. This is because a new log
folder will be created before the check.

This MR fixes this issue by loading the checkpoint before.

Fixes #1209 

## Type of change

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

## 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

@@ -63,6 +63,7 @@ Guidelines for modifications:
 * Rosario Scalise
 * Shafeef Omar
 * Vladimir Fokow
+* Wei Yang
 * Xavier Nal
 * Yang Jin
 * Zhengyu Zhang

source/standalone/workflows/rsl_rl/train.py

@@ -99,6 +99,11 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
 
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
+
+    # save resume path before creating a new log_dir
+    if agent_cfg.resume:
+        resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
+
     # wrap for video recording
     if args_cli.video:
         video_kwargs = {
@@ -122,10 +127,8 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
     # write git state to logs
     runner.add_git_repo_to_log(__file__)
-    # save resume path before creating a new log_dir
+    # load the checkpoint
     if agent_cfg.resume:
-        # get path to previous checkpoint
-        resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
         print(f"[INFO]: Loading model checkpoint from: {resume_path}")
         # load previously trained model
         runner.load(resume_path)