Refactor train and play scripts for single entry point

docs/source/features/hydra.rst

@@ -25,28 +25,28 @@ As a result, training with hydra arguments can be run with the following syntax:
 
         .. code-block:: shell
 
-            python scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
+            ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
 
     .. tab-item:: rl_games
         :sync: rl_games
 
         .. code-block:: shell
 
-            python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.params.seed=2024
+            ./isaaclab.sh train --library rl_games --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.params.seed=2024
 
     .. tab-item:: skrl
         :sync: skrl
 
         .. code-block:: shell
 
-            python scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
+            ./isaaclab.sh train --library skrl --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
 
     .. tab-item:: sb3
         :sync: sb3
 
         .. code-block:: shell
 
-            python scripts/reinforcement_learning/sb3/train.py --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
+            ./isaaclab.sh train --library sb3 --task=Isaac-Cartpole-v0 --headless env.actions.joint_effort.scale=10.0 agent.seed=2024
 
 The above command will run the training script with the task ``Isaac-Cartpole-v0`` in headless mode, and set the
 ``env.actions.joint_effort.scale`` parameter to 10.0 and the ``agent.seed`` parameter to 2024.
@@ -216,7 +216,7 @@ override is given:
 .. code-block:: bash
 
     # Use Newton physics backend
-    python train.py --task=Isaac-Reach-Franka-v0 env.physics=newton_mjwarp
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Reach-Franka-v0 env.physics=newton_mjwarp
 
 The ``default`` field can be set to ``None`` to make an optional feature that is
 disabled unless explicitly selected:
@@ -236,10 +236,10 @@ disabled unless explicitly selected:
 .. code-block:: bash
 
     # camera is None -- no camera overhead
-    python train.py --task=Isaac-Reach-Franka-v0
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Reach-Franka-v0
 
     # activate camera with the "large" preset
-    python train.py --task=Isaac-Reach-Franka-v0 env.scene.camera=large
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Reach-Franka-v0 env.scene.camera=large
 
 
 .. _hydra-backend-solver-presets:
@@ -299,10 +299,10 @@ is currently beta.
 .. code-block:: bash
 
     # Select the Kamino solver preset everywhere it is defined
-    python train.py --task=Isaac-Cartpole-v0 presets=newton_kamino
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-v0 presets=newton_kamino
 
     # Select the Kamino solver preset for a specific physics config path
-    python train.py --task=Isaac-Cartpole-v0 env.sim.physics=newton_kamino
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-v0 env.sim.physics=newton_kamino
 
 The ``newton_kamino`` preset is currently defined for ``Isaac-Cartpole-Direct-v0``,
 ``Isaac-Ant-Direct-v0``, ``Isaac-Cartpole-v0``, and ``Isaac-Ant-v0``. Passing
@@ -352,7 +352,7 @@ including inside dict-valued fields such as ``actuators``:
 .. code-block:: bash
 
     # Select MJWarp preset globally -- sets armature to 0.01
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 presets=newton_mjwarp
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 presets=newton_mjwarp
 
 
 Using Presets
@@ -362,29 +362,29 @@ Using Presets
 
 .. code-block:: bash
 
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 \
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 \
         env.events=newton_mjwarp
 
 **Global presets** -- apply the same preset name everywhere it exists:
 
 .. code-block:: bash
 
     # Apply "newton_mjwarp" preset to all configs that define it
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 \
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 \
         presets=newton_mjwarp
 
 **Multiple global presets** -- apply several non-conflicting presets:
 
 .. code-block:: bash
 
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 \
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 \
         presets=newton_mjwarp,inference
 
 **Combined** -- global presets + scalar overrides:
 
 .. code-block:: bash
 
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 \
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 \
         presets=newton_mjwarp \
         env.sim.dt=0.002
 
@@ -419,10 +419,10 @@ actuator armature is set to ``0.01``.
 .. code-block:: bash
 
     # Default (PhysX events, armature=0.0)
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0
 
     # MJWarp (Newton events, armature=0.01)
-    python train.py --task=Isaac-Velocity-Rough-Anymal-C-v0 presets=newton_mjwarp
+    ./isaaclab.sh train --library rsl_rl --task=Isaac-Velocity-Rough-Anymal-C-v0 presets=newton_mjwarp
 
 
 Summary

docs/source/features/multi_gpu.rst

@@ -96,14 +96,14 @@ To train with multiple GPUs, use the following command, where ``--nproc_per_node
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/train.py --library rl_games --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: rsl_rl
         :sync: rsl_rl
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/train.py --library rsl_rl --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: skrl
         :sync: skrl
@@ -115,14 +115,14 @@ To train with multiple GPUs, use the following command, where ``--nproc_per_node
 
                 .. code-block:: shell
 
-                    python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+                    python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed
 
             .. tab-item:: JAX
                 :sync: jax
 
                 .. code-block:: shell
 
-                    python -m skrl.utils.distributed.jax --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
+                    python -m skrl.utils.distributed.jax --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
 
 .. _multi-gpu-nccl-troubleshooting:
 
@@ -171,14 +171,14 @@ For the master node, use the following command, where ``--nproc_per_node`` repre
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library rl_games --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: rsl_rl
         :sync: rsl_rl
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library rsl_rl --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: skrl
         :sync: skrl
@@ -190,14 +190,14 @@ For the master node, use the following command, where ``--nproc_per_node`` repre
 
                 .. code-block:: shell
 
-                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed
 
             .. tab-item:: JAX
                 :sync: jax
 
                 .. code-block:: shell
 
-                    python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=0 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
+                    python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=0 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
 
 Note that the port (``5555``) can be replaced with any other available port.
 
@@ -211,14 +211,14 @@ For non-master nodes, use the following command, replacing ``--node_rank`` with
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library rl_games --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: rsl_rl
         :sync: rsl_rl
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library rsl_rl --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: skrl
         :sync: skrl
@@ -230,14 +230,14 @@ For non-master nodes, use the following command, replacing ``--node_rank`` with
 
                 .. code-block:: shell
 
-                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed
 
             .. tab-item:: JAX
                 :sync: jax
 
                 .. code-block:: shell
 
-                    python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=1 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
+                    python -m skrl.utils.distributed.jax --nproc_per_node=2 --nnodes=2 --node_rank=1 --coordinator_address=ip_of_master_machine:5555 scripts/reinforcement_learning/train.py --library skrl --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
 
 For more details on multi-node training with PyTorch, please visit the
 `PyTorch documentation <https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html>`_.

docs/source/features/population_based_training.rst

@@ -114,7 +114,7 @@ Launch *N* workers, where *n* indicates each worker index:
 .. code-block:: bash
 
    # Run this once per worker (n = 0..N-1), all pointing to the same directory/workspace
-   ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py \
+    ./isaaclab.sh train --library rl_games \
      --seed=<n> \
      --task=Isaac-Repose-Cube-Shadow-Direct-v0 \
      --num_envs=8192 \

docs/source/features/ray.rst

@@ -138,7 +138,7 @@ In a different terminal, run the following.
     --cfg_file scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py \
     --cfg_class CartpoleTheiaJobCfg \
     --run_mode local \
-    --workflow scripts/reinforcement_learning/rl_games/train.py \
+    --workflow scripts/reinforcement_learning/train.py --library rl_games \
     --num_workers_per_node <NUMBER_OF_GPUS_IN_COMPUTER>
 
 

docs/source/features/visualization.rst

@@ -63,20 +63,20 @@ Launch visualizers from the command line with ``--visualizer`` (or ``--viz`` ali
 .. code-block:: bash
 
     # Launch all visualizers (comma-delimited list, no spaces)
-    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --viz kit,newton,rerun
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0 --viz kit,newton,rerun
 
     # Launch only the Newton visualizer
-    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --viz newton
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0 --viz newton
 
     # Launch the Viser web-based visualizer
-    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --viz viser
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0 --viz viser
 
 
 To run in headless mode, omit the ``--viz`` argument:
 
 .. code-block:: bash
 
-    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0
 
 .. note::
 
@@ -491,7 +491,7 @@ the num of environments can be overwritten and decreased using ``--num_envs``:
 
 .. code-block:: bash
 
-    python scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Cartpole-v0 --viz rerun --num_envs 512
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0 --viz rerun --num_envs 512
 
 
 **Rerun Visualizer FPS Control**

docs/source/how-to/profile_with_nsys.rst

@@ -42,7 +42,7 @@ The following command shows how to capture a profile for the ``Isaac-Cartpole-v0
        -t nvtx,cuda \
        --python-functions-trace=scripts/benchmarks/nsys_trace.json \
        -o my_profile \
-       ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py \
+        ./isaaclab.sh train --library rsl_rl \
            --task=Isaac-Cartpole-v0 \
            --headless \
            --max_iterations=3

docs/source/how-to/record_video.rst

@@ -21,7 +21,7 @@ Example usage:
 
 .. code-block:: shell
 
-    python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --video --video_length 100 --video_interval 500
+    ./isaaclab.sh train --library rl_games --task=Isaac-Cartpole-v0 --headless --video --video_length 100 --video_interval 500
 
 
 The recorded videos will be saved in the same directory as the training checkpoints, under

docs/source/migration/migrating_from_isaacgymenvs.rst

@@ -916,7 +916,7 @@ To launch a training in Isaac Lab, use the command:
 
 .. code-block:: bash
 
-   python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 --headless
+   ./isaaclab.sh train --library rl_games --task=Isaac-Cartpole-Direct-v0 --headless
 
 Launching Inferencing
 ~~~~~~~~~~~~~~~~~~~~~
@@ -925,7 +925,7 @@ To launch inferencing in Isaac Lab, use the command:
 
 .. code-block:: bash
 
-   python scripts/reinforcement_learning/rl_games/play.py --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint=<path/to/checkpoint>
+   ./isaaclab.sh play --library rl_games --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint=<path/to/checkpoint>
 
 
 Additional Resources

docs/source/migration/migrating_from_omniisaacgymenvs.rst

@@ -983,7 +983,7 @@ To launch a training in Isaac Lab, use the command:
 
 .. code-block:: bash
 
-   python scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 --headless
+   ./isaaclab.sh train --library rl_games --task=Isaac-Cartpole-Direct-v0 --headless
 
 Launching Inferencing
 ~~~~~~~~~~~~~~~~~~~~~
@@ -992,7 +992,7 @@ To launch inferencing in Isaac Lab, use the command:
 
 .. code-block:: bash
 
-   python scripts/reinforcement_learning/rl_games/play.py --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint=<path/to/checkpoint>
+   ./isaaclab.sh play --library rl_games --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint=<path/to/checkpoint>
 
 
 .. _`OmniIsaacGymEnvs`: https://github.com/isaac-sim/OmniIsaacGymEnvs

docs/source/migration/migrating_to_isaaclab_3-0.rst

@@ -444,10 +444,10 @@ Pass ``presets=newton_mjwarp`` (or ``presets=physx``) on the CLI to swap the ent
 .. code-block:: bash
 
    # Run with Newton backend
-   python train.py task=Isaac-Franka-Cabinet-v0 presets=newton_mjwarp
+   ./isaaclab.sh train --library rsl_rl --task=Isaac-Franka-Cabinet-v0 presets=newton_mjwarp
 
    # Run with default (PhysX) backend
-   python train.py task=Isaac-Franka-Cabinet-v0
+   ./isaaclab.sh train --library rsl_rl --task=Isaac-Franka-Cabinet-v0
 
 Adding Multi-Backend Support to an Environment
 -----------------------------------------------

docs/source/overview/core-concepts/multi_backend_architecture.rst

@@ -161,10 +161,10 @@ Users then select the MJWarp Newton preset at the command line:
 .. code-block:: bash
 
     # Default (PhysX)
-    python train.py --task Isaac-Cartpole-v0
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0
 
     # MJWarp (Newton backend)
-    python train.py --task Isaac-Cartpole-v0 presets=newton_mjwarp
+    ./isaaclab.sh train --library rsl_rl --task Isaac-Cartpole-v0 presets=newton_mjwarp
 
 The Physics Manager
 -------------------

docs/source/overview/core-concepts/sensors/camera.rst

@@ -149,13 +149,13 @@ The active preset is selected at launch via the ``presets=`` CLI argument:
 .. code-block:: bash
 
    # Use Newton Warp renderer
-   python train.py task=Isaac-Cartpole-RGB-Camera-Direct-v0 presets=newton_renderer
+   ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-RGB-Camera-Direct-v0 presets=newton_renderer
 
    # Use OVRTX renderer
-   python train.py task=Isaac-Cartpole-RGB-Camera-Direct-v0 presets=ovrtx_renderer
+   ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-RGB-Camera-Direct-v0 presets=ovrtx_renderer
 
    # Use default (Isaac RTX)
-   python train.py task=Isaac-Cartpole-RGB-Camera-Direct-v0
+   ./isaaclab.sh train --library rsl_rl --task=Isaac-Cartpole-RGB-Camera-Direct-v0
 
 
 Accessing camera data
@@ -173,7 +173,7 @@ When using the RTX renderer, add ``--enable_cameras`` when launching:
 
 .. code-block:: shell
 
-    python scripts/reinforcement_learning/rl_games/train.py \
+    ./isaaclab.sh train --library rl_games \
         --task=Isaac-Cartpole-RGB-Camera-Direct-v0 --headless --enable_cameras