Package: joyrl
JoyRLJoyRLis a parallel reinforcement learning library based on PyTorch and Ray. Unlike existing RL libraries,JoyRLis helping users to release the burden of implementing algorithms with tough details, unfriendly APIs, and etc. JoyRL is designed for users to train and test RL algorithms withonly hyperparameters configuration, which is mush easier for beginners to learn and use. Also, JoyRL supports plenties of state-of-art RL algorithms includingRLHF(core of ChatGPT)(See algorithms below). JoyRL provides amodularized frameworkfor users as well to customize their own algorithms and environments.Install⚠️ Note that donot install JoyRL through any mirror image!!!# you need to install Anaconda firstcondacreate-njoyrlpython=3.8
condaactivatejoyrl
pipinstall-UjoyrlTorch install:Pip install is recommended, but if you encounter network error, you can try conda install or pip install with mirrors.# pip CPU onlypipinstalltorch==1.10.0torchvision==0.11.0torchaudio==0.10.0# if network error, then GPU with mirror imagepipinstalltorch==1.10.0+cu113torchvision==0.11.0+cu113torchaudio==0.10.0--extra-index-urlhttps://download.pytorch.org/whl/cu113# CPU onlycondainstallpytorch==1.10.0torchvision==0.11.0torchaudio==0.10.0cpuonly-cpytorch# GPUcondainstallpytorch==1.10.0torchvision==0.11.0torchaudio==0.10.0cudatoolkit=11.3-cpytorch-cconda-forgeUsageQuick Startthe following presents a demo to use joyrl. As you can see, first create a yaml file toconfig hyperparameters, then run the command as below in your terminal. That's all you need to do to train a DQN agent on CartPole-v1 environment.joyrl--yaml./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yamlor you can run the following code in your python file.importjoyrlif__name__=="__main__":print(joyrl.__version__)yaml_path="./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yaml"joyrl.run(yaml_path=yaml_path)DocumentationMore tutorials and API documentation are hosted onJoyRL docsorJoyRL 中文文档.AlgorithmsNameReferenceAuthorNotesQ-learningRL introductionjohnjim0816SarsaRL introductionjohnjim0816DQNDQN Paperjohnjim0816Double DQNDoubleDQN Paperjohnjim0816Dueling DQNDuelingDQN Paperjohnjim0816NoisyDQNNoisyDQN Paperjohnjim0816DDPGDDPG Paperjohnjim0816TD3TD3 Paperjohnjim0816PPOPPO Paperjohnjim0816Why JoyRL?RL PlatformGitHub Stars# of Alg.(1)Custom EnvAsync TrainingRNN SupportMulti-Head ObservationBackendBaselines9:heavy_check_mark: (gym):x::heavy_check_mark::x:TF1Stable-Baselines11:heavy_check_mark: (gym):x::heavy_check_mark::x:TF1Stable-Baselines37:heavy_check_mark: (gym):x::x::heavy_check_mark:PyTorchRay/RLlib16:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:TF/PyTorchSpinningUp6:heavy_check_mark: (gym):x::x::x:PyTorchDopamine7:x::x::x::x:TF/JAXACME14:heavy_check_mark: (dm_env):x::heavy_check_mark::heavy_check_mark:TF/JAXkeras-rl7:heavy_check_mark: (gym):x::x::x:Kerascleanrl9:heavy_check_mark: (gym):x::x::x:poetryrlpyt11:x::x::heavy_check_mark::heavy_check_mark:PyTorchChainerRL18:heavy_check_mark: (gym):x::heavy_check_mark::x:ChainerTianshou20:heavy_check_mark: (Gymnasium):x::heavy_check_mark::heavy_check_mark:PyTorchJoyRL9:heavy_check_mark: (Gymnasium):heavy_check_mark::heavy_check_mark::heavy_check_mark:PyTorchHere are some other highlghts of JoyRL:Provide a series of Chinese coursesJoyRL Book(with the English version in progress), suitable for beginners to start with a combination of theoryContributorsJohn JimPeking UniversityQi WangShanghai Jiao Tong UniversityYiyuan YangUniversity of Oxford