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

Denny Britz 92205f5e5b Merge pull request #198 from fspirit/fix-rendering-crash-on-win-10 2 months ago
DP a35df15268 Updated links to new version of Sutton's book 5 months ago
DQN 4a2df43bb1 fixed shape descriptions for neural network input layer 5 months ago
FA 92205f5e5b Merge pull request #198 from fspirit/fix-rendering-crash-on-win-10 2 months ago
Introduction a35df15268 Updated links to new version of Sutton's book 5 months ago
MC a35df15268 Updated links to new version of Sutton's book 5 months ago
MDP a35df15268 Updated links to new version of Sutton's book 5 months ago
PolicyGradient b2d179a1fe Update CliffWalk REINFORCE with Baseline Solution.ipynb 2 months ago
TD 1abaae41f6 Q-Learning docstring improvements. 4 months ago
lib 01b8b1379a nit 8 months ago
.gitignore 518a514333 Deep Q Updates 2 years ago
LICENSE 4ef051ba62 Add MIT License 2 years ago
README.md a35df15268 Updated links to new version of Sutton's book 5 months ago
__init__.py 15c739aa02 Add MC Control with Epsilon-Greedy Policies 3 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: