Skip to main content

A Library for Deep Reinforcement Learning

Project description

中文|EN

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

joyrl-0.1.7.tar.gz (3.4 kB view hashes)

Uploaded Source

Built Distribution

joyrl-0.1.7-py3-none-any.whl (3.2 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page