Metadata-Version: 2.1
Name: rl-games
Version: 1.1.4
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
        
        * 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](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
        
        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```
        
        To run Atari games or Box2d based environments training they need to be additionally installed with ```pip install gym[atari]``` or ```pip install gym[box2d]``` respectively.
        
        
        # 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```
        
        # Known issues
        
        * Running a single environment with Isaac Gym can cause crash, if it happens switch to at least 2 environments simulated in parallel
            
        1.1.3
        
        * Fixed crash when running single Isaac Gym environment in a play (test) mode.
        * Added config parameter ```clip_actions``` for switching off internal action clipping and rescaling
        
        1.1.4
        * Fixed crash in a play (test) mode in player, when simulation and rl_devices are not the same.
        * Fixed variuos multi gpu errors
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
