this is work that goes along with a book that is a work in progess.

Denny Britz cee9e78652 Merge pull request #188 from JovanSardinha/master 1 month ago
DP be7cfe308e just formatting 8 months ago
DQN fe3edfc570 fix #89 8 months ago
FA d8136b4c57 Updated links to new version of Sutton's book 1 year ago
Introduction d8136b4c57 Updated links to new version of Sutton's book 1 year ago
MC 542cbf04e5 Fix typo in MC Control 11 months ago
MDP d8136b4c57 Updated links to new version of Sutton's book 1 year ago
PolicyGradient 30326df0cf update value estimator only after calculating advantage 1 year ago
TD 8da669c149 Fix missing render() 1 year ago
lib 01b8b1379a nit 1 month ago
.gitignore 518a514333 Deep Q Updates 2 years ago
LICENSE 4ef051ba62 Add MIT License 2 years ago
README.md b47c9206b6 updates to README.md 1 month ago
__init__.py 15c739aa02 Add MC Control with Epsilon-Greedy Policies 2 years ago

README.md

Overview

This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from

Each folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.

All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.

Table of Contents

List of Implemented Algorithms

Resources

Textbooks:

Classes:

Talks/Tutorials:

Other Projects:

Selected Papers: