joyrl 0.1.7

A Library for Deep Reinforcement Learning

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JoyRL

Install

conda create -n easyrl python=3.7
conda activate easyrl
pip install -r requirements

Torch:

# CPU
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cpuonly -c pytorch
# GPU
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# GPU with mirrors
pip install torch==1.10.0+cu113 torchvision==0.11.0+cu113 torchaudio==0.10.0 --extra-index-url https://download.pytorch.org/whl/cu113

Usage

you can simply change the parameters (like env_name, algo_name) in config.config.GeneralConfig() and run:

python main.py

then it will a new folder named tasks to save results and models.

Or you can custom parameters with a yaml file as you can seen in config/custom_config_Train.yaml and run:

python main.py --yaml config/custom_config_Train.yaml

And there are presets yaml files in the defaults folder and well trained results in the benchmarks folder.

Algorithms

Name	Reference	Author	Notes
DQN	DQN Paper	johnjim0816