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

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

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 use RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.

• Introduction to RL problems, OpenAI gym
• MDPs and Bellman Equations
• Model-Based RL: Policy and Value Iteration using Dynamic Programming
• Model-Free Prediction & Control with Monte Carlo (MC)
• Model-Free Prediction & Control with Temporal Difference (TD)
• Function Approximation
• Deep Q Learning (WIP)
• Policy Gradient Methods (WIP)
• Learning and Planning (WIP)
• Exploration and Exploitation (WIP)

Textbooks:

• Reinforcement Learning: An Introduction (2nd Edition)

Classes:

• David Silver's Reinforcement Learning Course (UCL, 2015)
• CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)
• CS 8803 - Reinforcement Learning (Georgia Tech)

Talks/Tutorials:

• Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
• Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
• Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
• Tutorial: Introduction to Reinforcement Learning with Function Approximation
• John Schulman - Deep Reinforcement Learning (4 Lectures)

Other Projects:

• carpedm20/deep-rl-tensorflow
• matthiasplappert/keras-rl

Selected Papers:

• Human-Level Control through Deep Reinforcement Learning (2015-02)
• Deep Reinforcement Learning with Double Q-learning (2015-09)
• Prioritized Experience Replay (2015-11)
• Dueling Network Architectures for Deep Reinforcement Learning (2015-11)
• Asynchronous Methods for Deep Reinforcement Learning (2016-02)
• Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016-03)
• Mastering the game of Go with deep neural networks and tree search