Sutton & Barto Book: Reinforcement Learning: An Introduction
Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto Second Edition (see here for the first edition) MIT Press, Cambridge, MA, 2018 Buy from Amazon Errata Full Pdf pdf without margins (good for ipad) New Code Old Code Solutions -- send in your solutions for a chapter, get the official ones back (currently incomplete) Teaching Aids Literature sources cited in the book Latex
Deep Reinforcement Learning Online Course
The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods
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Reinforcement Learning - Source Code
GitHub - nimaous/reinfrocment-learning-agents: This is a python based simulation for single reinforcement learning agents
Deep Q-Network 論文輪読会
Deeplearning 4 j のクイックスタートガイド - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
