Skip to content
This repository has been archived by the owner on Oct 16, 2022. It is now read-only.
/ pytorch-rl Public archive

Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]

License

Notifications You must be signed in to change notification settings

bentrevett/pytorch-rl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

7f43f3d · Oct 23, 2020

History

39 Commits
Oct 23, 2020
Dec 4, 2019
Jan 27, 2020
Oct 20, 2020
Jan 27, 2020
Oct 22, 2020
Jan 27, 2020
Oct 20, 2020
Jan 27, 2020
Jan 27, 2020
Jan 27, 2020
Jan 27, 2020
Jan 27, 2020
Dec 14, 2019
Mar 18, 2019
Jan 27, 2020
Oct 23, 2020
Oct 23, 2020
Dec 14, 2019
Oct 23, 2020
Oct 23, 2020

Repository files navigation

PyTorch Reinforcement Learning

This repo contains tutorials covering reinforcement learning using PyTorch 1.3 and Gym 0.15.4 using Python 3.7.

If you find any mistakes or disagree with any of the explanations, please do not hesitate to submit an issue. I welcome any feedback, positive or negative!

Getting Started

To install PyTorch, see installation instructions on the PyTorch website.

To install Gym, see installation instructions on the Gym GitHub repo.

Tutorials

All tutorials use Monte Carlo methods to train the CartPole-v1 environment with the goal of reaching a total episode reward of 475 averaged over the last 25 episodes. There are also alternate versions of some algorithms to show how to use those algorithms with other environments.

Potential algorithms covered in future tutorials: DQN, ACER, ACKTR.

References