Materials for the reinforcement learning course at Data Science Retreat. This course is aimed at people with a grasp of supervised learning but no understanding of reinforcement learning.
The course materials are
This project is built and maintained by Adam Green - adam.green@adgefficiency.com.
This repo contains useful machine learning and reinforcement learning literature.
Sutton & Barto - Reinforcement Learning: An Introduction - 2nd Edition (in progress)
RL Course by David Silver - slides - lecture videos
Li (2017) Deep Reinforcement Learning: An Overview
For neural networks - Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
For everything else (linear models, random forests etc) - Elements of Staistical Learning - Trevor Hastie, Robert Tibshirani and Jerome Friedman
The Long-term of AI & Temporal-Difference Learning (Richard Sutton - DeepMind)
Deep Reinforcement Learning (John Schulman - OpenAI) - policy gradients
Deep Reinforcement Learning and Real World Challenges (Raia Hadsell - DeepMind)
Deep Reinforcement Learning in TensorFlow (Danijar Hafner - Stanford)
2017 NIPS David Silver Keynote - AlphaZero
Deep Reinforcement Learning: Pong from Pixels
Deep Deterministic Policy Gradients in TensorFlow
gym - Open AI
baselines - Open AI
rllab - Berkley