General infomation:

- program of the course : PDF version
- reference books
- a series of fun video examples of applications of reinforcement learning

Chapter 1 : Introduction, Bandits, and Combination of Experts for time series prediction

Chapter 2 : Learning dynamics (Bellman equation, Dynamic Programming, Monte Carlo, Temporal Difference(0), Q-learning, Sarsa)

Chapter 3 : Learning dynamics II (Eligibility traces, TD(lambda), generalization and function approximation, example with Atari player)

Chapter 4 : Entropy

Chapter 5 : Compression/Prediction/Generation equivalence

Chapter 6 : Kolmogorov complexity

Chapter 7 : Fisher information

Chapter 8 : Monte Carlo Tree Search (minimax trees, alpha-beta pruning, Upper Confidence Tree, applied to Go with CrazyStone/MoGo/AlphaGo) + Phi-MDP

Chapter 9 : Reinforcement learning based on information theory (e.g., KL-UCB, AIXI), and robotics

PS: we're searching for students on various topics, from machine learning to computer vision!

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