Reinforcement Learning Scripts

Reinforcement Learning Scripts#

We provide wrappers to different reinforcement libraries. These wrappers convert the data from the environments into the respective libraries function argument and return types.

RL-Games#

  • Training an agent with RL-Games on Isaac-Ant-v0:

    # install python module (for rl-games)
./isaaclab.sh -i rl_games
# run script for training
./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-Ant-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth
# run script for playing a pre-trained checkpoint with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --num_envs 32 --use_pretrained_checkpoint
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py --task Isaac-Ant-v0 --headless --video --video_length 200

    :: install python module (for rl-games)
isaaclab.bat -i rl_games
:: run script for training
isaaclab.bat -p scripts\reinforcement_learning\rl_games\train.py --task Isaac-Ant-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth
:: run script for playing a pre-trained checkpoint with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --num_envs 32 --use_pretrained_checkpoint
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p scripts\reinforcement_learning\rl_games\play.py --task Isaac-Ant-v0 --headless --video --video_length 200

RSL-RL#

  • Training an agent with RSL-RL on Isaac-Reach-Franka-v0:

    # install python module (for rsl-rl)
./isaaclab.sh -i rsl_rl
# run script for training
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Reach-Franka-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint model.pt
# run script for playing a pre-trained checkpoint with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200

    :: install python module (for rsl-rl)
isaaclab.bat -i rsl_rl
:: run script for training
isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\train.py --task Isaac-Reach-Franka-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint model.pt
:: run script for playing a pre-trained checkpoint with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p scripts\reinforcement_learning\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200

SKRL#

  • Training an agent with SKRL on Isaac-Reach-Franka-v0:

    # install python module (for skrl)
./isaaclab.sh -i skrl
# run script for training
./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Reach-Franka-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
# run script for playing a pre-trained checkpoint with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200

    :: install python module (for skrl)
isaaclab.bat -i skrl
:: run script for training
isaaclab.bat -p scripts\reinforcement_learning\skrl\train.py --task Isaac-Reach-Franka-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
:: run script for playing a pre-trained checkpoint with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --use_pretrained_checkpoint
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200

    Warning

    It is recommended to install JAX manually before proceeding to install skrl and its dependencies, as JAX installs its CPU version by default. Visit the skrl installation page for more details. Note that JAX GPU support is only available on Linux.

    JAX 0.6.0 or higher (built on CuDNN v9.8) is incompatible with Isaac Lab’s PyTorch 2.7 (built on CuDNN v9.7), and therefore not supported. To install a compatible version of JAX for CUDA 12 use pip install "jax[cuda12]<0.6.0", for example.

    # install python module (for skrl)
./isaaclab.sh -i skrl
# install skrl dependencies for JAX
./isaaclab.sh -p -m pip install skrl["jax"]
# install jax<0.6.0 for torch 2.7
./isaaclab.sh -p -m pip install "jax[cuda12]<0.6.0"
# run script for training
./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax
# run script for playing with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32  --ml_framework jax --checkpoint /PATH/TO/model.pt
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax --video --video_length 200

    • Training the multi-agent environment Isaac-Shadow-Hand-Over-Direct-v0 with skrl:

    # install python module (for skrl)
./isaaclab.sh -i skrl
# run script for training with the MAPPO algorithm (IPPO is also supported)
./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Shadow-Hand-Over-Direct-v0 --headless --algorithm MAPPO
# run script for playing with 32 environments with the MAPPO algorithm (IPPO is also supported)
./isaaclab.sh -p scripts/reinforcement_learning/skrl/play.py --task Isaac-Shadow-Hand-Over-Direct-v0 --num_envs 32 --algorithm MAPPO --checkpoint /PATH/TO/model.pt

    :: install python module (for skrl)
isaaclab.bat -i skrl
:: run script for training with the MAPPO algorithm (IPPO is also supported)
isaaclab.bat -p scripts\reinforcement_learning\skrl\train.py --task Isaac-Shadow-Hand-Over-Direct-v0 --headless --algorithm MAPPO
:: run script for playing with 32 environments with the MAPPO algorithm (IPPO is also supported)
isaaclab.bat -p scripts\reinforcement_learning\skrl\play.py --task Isaac-Shadow-Hand-Over-Direct-v0 --num_envs 32 --algorithm MAPPO --checkpoint /PATH/TO/model.pt

Stable-Baselines3#

  • Training an agent with Stable-Baselines3 on Isaac-Velocity-Flat-Unitree-A1-v0:

    # install python module (for stable-baselines3)
./isaaclab.sh -i sb3
# run script for training
./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
# run script for playing a pre-trained checkpoint with 32 environments
./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200

    :: install python module (for stable-baselines3)
isaaclab.bat -i sb3
:: run script for training
isaaclab.bat -p scripts\reinforcement_learning\sb3\train.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
:: run script for playing a pre-trained checkpoint with 32 environments
isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --num_envs 32 --use_pretrained_checkpoint
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p scripts\reinforcement_learning\sb3\play.py --task Isaac-Velocity-Flat-Unitree-A1-v0 --headless --video --video_length 200

All the scripts above log the training progress to Tensorboard in the logs directory in the root of the repository. The logs directory follows the pattern logs/<library>/<task>/<date-time>, where <library> is the name of the learning framework, <task> is the task name, and <date-time> is the timestamp at which the training script was executed.

To view the logs, run:

# execute from the root directory of the repository
./isaaclab.sh -p -m tensorboard.main --logdir=logs

:: execute from the root directory of the repository
isaaclab.bat -p -m tensorboard.main --logdir=logs