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Contents
Introduction
Foreword
What is reinforcement learning?
Getting started with a first example
Single-agent Reinforcement Learning
Markov Decision Processes
Value-based methods
Value Iteration
Multi-armed bandits
Temporal difference reinforcement learning
n-step reinforcement learning
Monte-Carlo Tree Search (MCTS)
Q-function approximation
Reward shaping
Policy-based methods
Policy iteration
Policy gradients
Actor-critic methods
Modelling and abstraction for MDPs
Multi-agent Reinforcement Learning
Normal form games
Extensive form games
Backward induction
Multi-agent reinforcement learning
Modelling and abstraction for multi-agent games
Appendix
Introduction to basic probability theory
Repository
Open issue
.md
.pdf
Experience replay
Experience replay
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