RL Games: High performance RL library

Papers and related links

• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning: https://arxiv.org/abs/2108.10470
• Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger: https://s2r2-ig.github.io/ https://arxiv.org/abs/2108.09779
• Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? https://arxiv.org/abs/2011.09533

Some results on interesting environments

• NVIDIA Isaac Gym

• Starcraft 2 Multi Agents
• BRAX
• Old TF1.x results

Config file

• Configuration

Implemented in Pytorch:

• PPO with the support of asymmetric actor-critic variant
• Support of end-to-end GPU accelerated training pipeline with Isaac Gym and Brax
• Masked actions support
• Multi-agent training, decentralized and centralized critic variants
• Self-play

Implemented in Tensorflow 1.x (not updates now):

• Rainbow DQN
• A2C
• PPO

Installation

For maximum training performance a preliminary installation of Pytorch 1.9+ with CUDA 11.1 is highly recommended:

conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c nvidia or: pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.htm

Then:

pip install rl-games

Training

NVIDIA Isaac Gym

Download and follow the installation instructions from https://developer.nvidia.com/isaac-gym
Run from python/rlgpu directory:

Ant
python rlg_train.py --task Ant --headless
python rlg_train.py --task Ant --play --checkpoint nn/Ant.pth --num_envs 100

Humanoid
python rlg_train.py --task Humanoid --headless
python rlg_train.py --task Humanoid --play --checkpoint nn/Humanoid.pth --num_envs 100

Shadow Hand block orientation task
python rlg_train.py --task ShadowHand --headless
python rlg_train.py --task ShadowHand --play --checkpoint nn/ShadowHand.pth --num_envs 100

Atari Pong
python runner.py --train --file rl_games/configs/atari/ppo_pong.yaml
python runner.py --play --file rl_games/configs/atari/ppo_pong.yaml --checkpoint nn/PongNoFrameskip.pth

Brax Ant
python runner.py --train --file rl_games/configs/brax/ppo_ant.yaml
python runner.py --play --file rl_games/configs/atari/ppo_ant.yaml --checkpoint nn/Ant_brax.pth

Release Notes

1.1.0

• Added to pypi: pip install rl-games
• Added reporting env (sim) step fps, without policy inference. Improved naming.
• Renames in yaml config for better readability: steps_num to horizon_length amd lr_threshold to kl_threshold

Troubleshouting

• Some of the supported envs are not installed with setup.py, you need to manually install them
• Starting from rl-games 1.1.0 old yaml configs won't be compatible with the new version:
  • steps_num should be changed to horizon_length amd lr_threshold to kl_threshold