Metadata-Version: 2.1
Name: rl-games
Version: 1.0.2
Summary: UNKNOWN
Home-page: https://github.com/Denys88/rl_games
Author: Denys Makoviichuk, Viktor Makoviichuk
Author-email: trrrrr97@gmail.com, victor.makoviychuk@gmail.com
License: MIT
Description: # RL Games: High performance RL library  
        
        ## Papers and related links
        
        * 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](docs/ISAAC_GYM.md)
        
        ![Ant_running](https://user-images.githubusercontent.com/463063/125260924-a5969800-e2b5-11eb-931c-116cc90d4bbe.gif)
        ![Humanoid_running](https://user-images.githubusercontent.com/463063/125266095-4edf8d00-e2ba-11eb-9c1a-4dc1524adf71.gif)
        
        ![Allegro_Hand_400](https://user-images.githubusercontent.com/463063/125261559-38373700-e2b6-11eb-80eb-b250a0693f0b.gif)
        ![Shadow_Hand_OpenAI](https://user-images.githubusercontent.com/463063/125262637-328e2100-e2b7-11eb-99af-ea546a53f66a.gif)
        
        * [Starcraft 2 Multi Agents](docs/SMAC.md)  
        * [BRAX](docs/BRAX.md)  
        * [Old TF1.x results](docs/BRAX.md)  
        
        ## Config file  
        
        * [Configuration](docs/CONFIG_PARAMS.md)  
        
        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
        Clone repo and run:
        ```pip install -e .```
        
        Or:
        ```pip install git+https://github.com/Denys88/rl_games.git```
        
        # 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``` 
        
        # Troubleshouting
        
        * Some of the supported envs are not installed with setup.py, you need to manually install them
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
