Resources

Books & Papers

• “Reinforcement Learning: An introduction,” Second edition, in progress, by Richard S. Sutton and Andrew G. book
  

• “Algorithms for Reinforcement Learning,” by Csaba Szepesvari book
  

• “Reinforcement Learning and Dynamic Programming Using Function Approximators by Busoniu,” Babuska, De Schutter and Ernst book

• “Perspectives of Approximate Dynamic Programming,” W. B. Powell, Annals of Operations Research, Annals of Operations Research (2012), Springer. tutorial

• “Convergence of Q-learning: a simple proof,” by Melo paper

• “Deterministic Policy Gradient Algorithms,” by Silver, Lever, Heess, Degris, Wierstra, Riedmiller, 2014 paper appendices

Video Lectures

• Reinforcement Learning Course by David Silver Video Lectures
  

Courses

• Berkeley's Deep Reinforcement Learning course CS 294 website including additional materials, presentations, video lectures
  

• Course on Reinforcement Learning by Alessandro Lazaric from from the Electronic and Informatics Department of Politecnico di Milano website
  

• Deep Reinforcement Learning and Control CMU 10703 website
  

• Reinforcement Learning, Stanford, MS&E338, Benjamin Van Roy website

Optional paper for the project implementation

• Continuous control with deep reinforcement learning Lillicrap a. al. paper

• Benchmarking Deep Reinforcement Learning for Continuous Control Duan et al paper

RL enviourment test and code

• OpenAI Gym: a toolkit for developing and comparing reinforcement learning algorithms link

• TensorForce: Code and enviourment TensorForce