Contents

I: INTRODUCTION

• Foreword
• What is reinforcement learning?
• Getting started with a first example

II: VALUE-BASED METHODS

• Introduction to value-based methods
• Markov Decision Processes
• Value Iteration
• Multi-armed bandits
• Temporal difference reinforcement learning
• Monte-Carlo Tree Search (MCTS)

III: POLICY-BASED METHODS

• Introduction to policy-based methods
• Policy iteration
• Policy gradients
• Actor-critic methods

IV: SCALING

• Q-function approximation
• Reward shaping
• n-step reinforcement learning
• Experience replay
• Modelling and abstraction for MDPs

V: MULTI-AGENT REINFORCEMENT LEARNING

• Normal form games
• Extensive form games
• Backward induction
• Multi-agent reinforcement learning
• Modelling and abstraction for multi-agent games

VI: APPENDIX

• Introduction to basic probability theory

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