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OverviewReinforcement Learning

Reinforcement Learning Scripts

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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

When using RL-Games with the Ray workflow for distributed training or hyperparameter tuning, please be aware that due to security risks associated with Ray, this workflow is not intended for use outside of a strictly controlled network environment.

  • 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

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 /PATH/TO/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
  • Training and distilling an agent with RSL-RL on Isaac-Velocity-Flat-Anymal-D-v0:
# install python module (for rsl-rl)
./isaaclab.sh -i rsl_rl
# run script for rl training of the teacher agent
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless
# run script for distilling the teacher agent into a student agent
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --headless --agent rsl_rl_distillation_cfg_entry_point --load_run teacher_run_folder_name
# run script for playing the student with 64 environments
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Velocity-Flat-Anymal-D-v0 --num_envs 64 --agent rsl_rl_distillation_cfg_entry_point

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
  • 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

Stable-Baselines3

# 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

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