Updates play script for SB3 and RL library benchmarks (#2789)

# Description

Minor fixes for SB3 play script to accommodate for inference task names
and updates profiling numbers for the RL library comparison.

## Type of change

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applicable. -->

- Bug fix (non-breaking change which fixes an issue)
- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] 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
- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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- [x] I have done this task
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docs/source/overview/environments.rst

@@ -214,6 +214,12 @@ We provide environments for both disassembly and assembly.
       wget https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run
       sudo sh cuda_12.8.0_570.86.10_linux.run
 
+  When using conda, cuda toolkit can be installed with:
+
+  .. code-block:: bash
+
+      conda install cudatoolkit
+
   For addition instructions and Windows installation, please refer to the `CUDA installation page <https://developer.nvidia.com/cuda-12-8-0-download-archive>`_.
 
 * |disassembly-link|: The plug starts inserted in the socket. A low-level controller lifts the plug out and moves it to a random position. This process is purely scripted and does not involve any learned policy. Therefore, it does not require policy training or evaluation. The resulting trajectories serve as demonstrations for the reverse process, i.e., learning to assemble. To run disassembly for a specific task: ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py --assembly_id=ASSEMBLY_ID --disassembly_dir=DISASSEMBLY_DIR``. All generated trajectories are saved to a local directory ``DISASSEMBLY_DIR``.

docs/source/overview/reinforcement-learning/rl_frameworks.rst

@@ -71,20 +71,18 @@ Training Performance
 --------------------
 
 We performed training with each RL library on the same ``Isaac-Humanoid-v0`` environment
-with ``--headless`` on a single RTX 4090 GPU using 4096 environments
+with ``--headless`` on a single RTX PRO 6000 GPU using 4096 environments
 and logged the total training time for 65.5M steps for each RL library.
 
-..
-  Note: SB3 need to be re-run (current number comes from a GeForce RTX 3070)
 
 +--------------------+-----------------+
 | RL Library         | Time in seconds |
 +====================+=================+
-| RL-Games           | 203             |
+| RL-Games           | 207             |
 +--------------------+-----------------+
-| SKRL               | 204             |
+| SKRL               | 208             |
 +--------------------+-----------------+
-| RSL RL             | 207             |
+| RSL RL             | 199             |
 +--------------------+-----------------+
-| Stable-Baselines3  | 550             |
+| Stable-Baselines3  | 322             |
 +--------------------+-----------------+

scripts/reinforcement_learning/sb3/play.py

@@ -82,13 +82,14 @@ def main():
     )
 
     task_name = args_cli.task.split(":")[-1]
+    train_task_name = task_name.replace("-Play", "")
 
     # directory for logging into
-    log_root_path = os.path.join("logs", "sb3", task_name)
+    log_root_path = os.path.join("logs", "sb3", train_task_name)
     log_root_path = os.path.abspath(log_root_path)
     # checkpoint and log_dir stuff
     if args_cli.use_pretrained_checkpoint:
-        checkpoint_path = get_published_pretrained_checkpoint("sb3", task_name)
+        checkpoint_path = get_published_pretrained_checkpoint("sb3", train_task_name)
         if not checkpoint_path:
             print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
             return
@@ -98,7 +99,7 @@ def main():
             checkpoint = "model_.*.zip"
         else:
             checkpoint = "model.zip"
-        checkpoint_path = get_checkpoint_path(log_root_path, ".*", checkpoint)
+        checkpoint_path = get_checkpoint_path(log_root_path, ".*", checkpoint, sort_alpha=False)
     else:
         checkpoint_path = args_cli.checkpoint
     log_dir = os.path.dirname(checkpoint_path)