ChanBong/reinforcement_learning

Overview

Implementation of popular Reinforecement Learning algorithms and problems. These are learning tools that should go hand in hand with textbooks and/or video lectures to have good theoratical background

• Reinforcement Learning: An Introduction (2nd Edition)
• David Silver's Reinforcement Learning Course

Each folder in itself corresponds to one or more topics in RL that matches with standard textboooks. 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 Pytorch for neural network implementations.

Table of Contents

• Introduction to RL problems & OpenAI Gym

List of Implemented Algorithms

Resources

Textbooks:

• Reinforcement Learning: An Introduction (2nd Edition)
• Algorithms for Reinforcement Learning

Classes:

• David Silver's Reinforcement Learning Course (UCL, 2015)
• 2021 DeepMind x UCL Lecture Series (UCL, 2021)
• CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)
• CS 8803 - Reinforcement Learning (Georgia Tech)
• CS885 - Reinforcement Learning (UWaterloo), Spring 2018
• CS294-112 - Deep Reinforcement Learning (UC Berkeley)

Blogs:

• A (Long) Peek into Reinforcement Learning

Similar Repos

• https://github.com/dennybritz/reinforcement-learning