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1. Books

• Reinforcement Learning: An Introduction
• Algorithms for Reinforcement Learning
• Markov Decision Processes Discrete Stochastic Dynamic Programming
• Convex Optimization

2. Courses

• Rich Sutton (Alberta)
• David Silver (UCL)
• Reinforcement Learning (Stanford)
• Deep Reinforcement Learning (UC Berkeley)
• Hongyi Li (Taiwan)
• Deep Reinforcement Learning and Control (CMU)
• Multi-Agent Reinforcement Learning Tutorial (SJTU)

3. Platform and Codes

• Open AI
• 莫烦 Python
• Algorithm codes (DP, MC, SARSA, Q-Learning, PG, DDPG, A3C)
• Python replication for Sutton & Barto’s book
• Awesome Deep Reinforcement Learning
• [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl#codes