rl_training

Tutorial Videos

We've released the following tutorials for training and deploying a reinforcement learning policy. Please check it out!

• 【四足运控，从入门到精通】第一期 | 从头训练测试部署一个强化学习策略 (Bilibili)
• Quadruped Robot Control | From Beginner to Pro Episode 1 (YouTube)

Overview

rl_training is a RL training library for deeprobotics robots, based on IsaacLab. The table below lists all available environments:

Robot Model	Environment Name (ID)	Screenshot
Deeprobotics Lite3	Rough-Deeprobotics-Lite3-v0
Deeprobotics M20	Rough-Deeprobotics-M20-v0

Note

If you want to deploy policies in mujoco or real robots, please use the corresponding deploy repo in Deep Robotics Github Center.

Installation

• Install Isaac Lab by following the installation guide. We recommend using the conda installation as it simplifies calling Python scripts from the terminal.

• Clone this repository separately from the Isaac Lab installation (i.e. outside the IsaacLab directory):

  git clone https://github.com/DeepRoboticsLab/rl_training.git

• Using a python interpreter that has Isaac Lab installed, install the library

  python -m pip install -e source/rl_training

• Verify that the extension is correctly installed by running the following command to print all the available environments in the extension:

  python scripts/tools/list_envs.py

Setup as Omniverse Extension (Optional, click to expand)

We provide an example UI extension that will load upon enabling your extension defined in source/rl_training/rl_training/ui_extension_example.py.

To enable your extension, follow these steps:

1. Add the search path of your repository to the extension manager:

   • Navigate to the extension manager using Window -> Extensions.
   • Click on the Hamburger Icon (☰), then go to Settings.
   • In the Extension Search Paths, enter the absolute path to rl_trainingb/source
   • If not already present, in the Extension Search Paths, enter the path that leads to Isaac Lab's extension directory directory (IsaacLab/source)
   • Click on the Hamburger Icon (☰), then click Refresh.

2. Search and enable your extension:

   • Find your extension under the Third Party category.
   • Toggle it to enable your extension.

Try examples

Deeprobotics Lite3:

# Train
python scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-Lite3-v0 --headless

# Play
python scripts/reinforcement_learning/rsl_rl/play.py --task=Rough-Deeprobotics-Lite3-v0 --num_envs=10

Deeprobotics M20:

# Train
python scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-M20-v0 --headless

# Play
python scripts/reinforcement_learning/rsl_rl/play.py --task=Rough-Deeprobotics-M20-v0 --num_envs=10

Note

If you want to control a SINGLE ROBOT with the keyboard during playback, add --keyboard at the end of the play script.

Key bindings:
====================== ========================= ========================
Command                Key (+ve axis)            Key (-ve axis)
====================== ========================= ========================
Move along x-axis      Numpad 8 / Arrow Up       Numpad 2 / Arrow Down
Move along y-axis      Numpad 4 / Arrow Right    Numpad 6 / Arrow Left
Rotate along z-axis    Numpad 7 / Z              Numpad 9 / X
====================== ========================= ========================

• You can change Rough to Flat in the above configs.

• Record video of a trained agent (requires installing ffmpeg), add --video --video_length 200

• Play/Train with 32 environments, add --num_envs 32

• Play on specific folder or checkpoint, add --load_run run_folder_name --checkpoint model.pt

• Resume training from folder or checkpoint, add --resume --load_run run_folder_name --checkpoint model.pt

• To train with multiple GPUs, use the following command, where --nproc_per_node represents the number of available GPUs:

  python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless 
  python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-Lite3-v0 --headless --distributed --num_envs=2048

• Note: each gpu will have the same number of envs specified in the config, to use the previous total number of envs, devide it by the number of gpus.

• To scale up training beyond multiple GPUs on a single machine, it is also possible to train across multiple nodes. To train across multiple nodes/machines, it is required to launch an individual process on each node.

  For the master node, use the following command, where --nproc_per_node represents the number of available GPUs, and --nnodes represents the number of nodes:

  python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed

  Note that the port (5555) can be replaced with any other available port. For non-master nodes, use the following command, replacing --node_rank with the index of each machine:

  python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed

Tensorboard

To view tensorboard, run:

tensorboard --logdir=logs

Troubleshooting

Pylance Missing Indexing of Extensions

In some VsCode versions, the indexing of part of the extensions is missing. In this case, add the path to your extension in .vscode/settings.json under the key "python.analysis.extraPaths".

Note: Replace <path-to-isaac-lab> with your own IsaacLab path.

{
    "python.languageServer": "Pylance",
    "python.analysis.extraPaths": [
        "${workspaceFolder}/source/rl_training",
        "/<path-to-isaac-lab>/source/isaaclab",
        "/<path-to-isaac-lab>/source/isaaclab_assets",
        "/<path-to-isaac-lab>/source/isaaclab_mimic",
        "/<path-to-isaac-lab>/source/isaaclab_rl",
        "/<path-to-isaac-lab>/source/isaaclab_tasks",
    ]
}

Clean USD Caches

Temporary USD files are generated in /tmp/IsaacLab/usd_{date}_{time}_{random} during simulation runs. These files can consume significant disk space and can be cleaned by:

rm -rf /tmp/IsaacLab/usd_*

Acknowledgements

The project uses some code from the following open-source code repositories:

• fan-ziqi/robot_lab