keavnn/rls repository overview

conda env create -f environment.yaml

$ git clone https://github.com/StepNeverStop/RLs.git
$ cd RLs
$ conda create -n rls python=3.6
$ conda activate rls
# Windows
$ pip install -e .[windows]
# Linux or Mac OS
$ pip install -e .

$ pip install -e .[unity]

$ pip install -e .[atari]

$ docker pull keavnn/rls:latest

"""
Usage:
    python [options]

Options:
    -h,--help                   显示帮助
    -a,--algorithm=<name>       算法
                                specify the training algorithm [default: ppo]
    -c,--copys=<n>              指定并行训练的数量
                                nums of environment copys that collect data in parallel [default: 1]
    -e,--env=<file>             指定Unity环境路径
                                specify the path of builded training environment of UNITY3D [default: None]
    -g,--graphic                是否显示图形界面
                                whether show graphic interface when using UNITY3D [default: False]
    -i,--inference              推断
                                inference the trained model, not train policies [default: False]
    -m,--models=<n>             同时训练多少个模型
                                specify the number of trails that using different random seeds [default: 1]
    -n,--name=<name>            训练的名字
                                specify the name of this training task [default: None]
    -p,--port=<n>               端口
                                specify the port that communicate with training environment of UNITY3D [default: 5005]
    -r,--rnn                    是否使用RNN模型
                                whether use rnn[GRU, LSTM, ...] or not [default: False]
    -s,--save-frequency=<n>     保存频率
                                specify the interval that saving model checkpoint [default: None]
    -t,--train-step=<n>         总的训练次数
                                specify the training step that optimize the policy model [default: None]
    -u,--unity                  是否使用unity客户端
                                whether training with UNITY3D editor [default: False]
    
    --apex=<str>                i.e. "learner"/"worker"/"buffer"/"evaluator" [default: None]
    --unity-env=<name>          指定unity环境的名字
                                specify the name of training environment of UNITY3D [default: None]
    --config-file=<file>        指定模型的超参数config文件
                                specify the path of training configuration file [default: None]
    --store-dir=<file>          指定要保存模型、日志、数据的文件夹路径
                                specify the directory that store model, log and others [default: None]
    --seed=<n>                  指定训练器全局随机种子
                                specify the random seed of module random, numpy and tensorflow [default: 42]
    --unity-env-seed=<n>        指定unity环境的随机种子
                                specify the environment random seed of UNITY3D [default: 42]
    --max-step=<n>              每回合最大步长
                                specify the maximum step per episode [default: None]
    --train-episode=<n>         总的训练回合数
                                specify the training maximum episode [default: None]
    --train-frame=<n>           总的训练采样次数
                                specify the training maximum steps interacting with environment [default: None]
    --load=<name>               指定载入model的训练名称
                                specify the name of pre-trained model that need to load [default: None]
    --prefill-steps=<n>         指定预填充的经验数量
                                specify the number of experiences that should be collected before start training, use for off-policy algorithms [default: None]
    --prefill-choose            指定no_op操作时随机选择动作，或者置0
                                whether choose action using model or choose randomly [default: False]
    --gym                       是否使用gym训练环境
                                whether training with gym [default: False]
    --gym-env=<name>            指定gym环境的名字
                                specify the environment name of gym [default: CartPole-v0]
    --gym-env-seed=<n>          指定gym环境的随机种子
                                specify the environment random seed of gym [default: 42]
    --render-episode=<n>        指定gym环境从何时开始渲染
                                specify when to render the graphic interface of gym environment [default: None]
    --info=<str>                抒写该训练的描述，用双引号包裹
                                write another information that describe this training task [default: None]
    --hostname                  是否在训练名称后附加上主机名称
                                whether concatenate hostname with the training name [default: False]
    --no-save                   指定是否在训练中保存模型、日志及训练数据
                                specify whether save models/logs/summaries while training or not [default: False]
Example:
    gym:
        python run.py --gym -a dqn --gym-env CartPole-v0 -c 12 -n dqn_cartpole --no-save
    unity:
        python run.py -u -a ppo -n run_with_unity
        python run.py -e /root/env/3dball.app -a sac -n run_with_execution_file
"""

@misc{RLs,
  author = {Keavnn},
  title = {RLs: Reinforcement Learning research framework for Unity3D and Gym},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/StepNeverStop/RLs}},
}