Environments to use

1. PyBullet_envs
2. Mujoco

Courses

• Geoff Hinton on Coursera
• Andrew Ng on Coursera
• David Silver's course on reinforcement learning
• Nando de Freitas' course on machine learning
• Andrej Karpathy's course on neural networks

Relevant Textbooks

Copies of these are reserved in the UdeM library.

• Deep Learning
• Sutton & Barto, Reinforcement Learning: An Introduction (second edition)
• Szepesvari, Algorithms for Reinforcement Learning
• Bertsekas, Dynamic Programming and Optimal Control, Vols I and II
• Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming
• Powell, Approximate Dynamic Programming
• An Introduction to Deep Reinforcement Learning

1. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) 1st Edition
   1. This books might be a good start to look at but It only seems to cover up to DQN.