Reinforcement Learning Wrappers

Reinforcement Learning Wrappers#

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 source/standalone/workflows/rl_games/train.py --task Isaac-Ant-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p source/standalone/workflows/rl_games/play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p source/standalone/workflows/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 source\standalone\workflows\rl_games\train.py --task Isaac-Ant-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p source\standalone\workflows\rl_games\play.py --task Isaac-Ant-v0 --num_envs 32 --checkpoint /PATH/TO/model.pth
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p source\standalone\workflows\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 source/standalone/workflows/rsl_rl/train.py --task Isaac-Reach-Franka-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p source/standalone/workflows/rsl_rl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint model.pt
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p source/standalone/workflows/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 source\standalone\workflows\rsl_rl\train.py --task Isaac-Reach-Franka-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p source\standalone\workflows\rsl_rl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --load_run run_folder_name --checkpoint model.pt
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p source\standalone\workflows\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 source/standalone/workflows/skrl/train.py --task Isaac-Reach-Franka-v0 --headless
# run script for playing with 32 environments
./isaaclab.sh -p source/standalone/workflows/skrl/play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p source/standalone/workflows/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 source\standalone\workflows\skrl\train.py --task Isaac-Reach-Franka-v0 --headless
:: run script for playing with 32 environments
isaaclab.bat -p source\standalone\workflows\skrl\play.py --task Isaac-Reach-Franka-v0 --num_envs 32 --checkpoint /PATH/TO/model.pt
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p source\standalone\workflows\skrl\play.py --task Isaac-Reach-Franka-v0 --headless --video --video_length 200

    # install python module (for skrl)
./isaaclab.sh -i skrl
# install skrl dependencies for JAX. Visit https://skrl.readthedocs.io/en/latest/intro/installation.html for more details
./isaaclab.sh -p -m pip install skrl["jax"]
# run script for training
./isaaclab.sh -p source/standalone/workflows/skrl/train.py --task Isaac-Reach-Franka-v0 --headless --ml_framework jax
# run script for playing with 32 environments
./isaaclab.sh -p source/standalone/workflows/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 source/standalone/workflows/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 source/standalone/workflows/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 source/standalone/workflows/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 source\standalone\workflows\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 source\standalone\workflows\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-Cartpole-v0:

    # install python module (for stable-baselines3)
./isaaclab.sh -i sb3
# run script for training
# note: we set the device to cpu since SB3 doesn't optimize for GPU anyway
./isaaclab.sh -p source/standalone/workflows/sb3/train.py --task Isaac-Cartpole-v0 --headless --device cpu
# run script for playing with 32 environments
./isaaclab.sh -p source/standalone/workflows/sb3/play.py --task Isaac-Cartpole-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
# run script for recording video of a trained agent (requires installing `ffmpeg`)
./isaaclab.sh -p source/standalone/workflows/sb3/play.py --task Isaac-Cartpole-v0 --headless --video --video_length 200

    :: install python module (for stable-baselines3)
isaaclab.bat -i sb3
:: run script for training
:: note: we set the device to cpu since SB3 doesn't optimize for GPU anyway
isaaclab.bat -p source\standalone\workflows\sb3\train.py --task Isaac-Cartpole-v0 --headless --device cpu
:: run script for playing with 32 environments
isaaclab.bat -p source\standalone\workflows\sb3\play.py --task Isaac-Cartpole-v0 --num_envs 32 --checkpoint /PATH/TO/model.zip
:: run script for recording video of a trained agent (requires installing `ffmpeg`)
isaaclab.bat -p source\standalone\workflows\sb3\play.py --task Isaac-Cartpole-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