Reinforcement Learning and Decision Making

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Chapter 1 — Introduction to Reinforcement Learning and Decision Making

Lecture Slides   |   Lecture Notes   |   Exercises   |   Answers NEW   |   Python Code

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Chapter 2 — Markov Decision Processes (MDPs); states, actions, transition dynamics, reward functions, and policies

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Chapter 3—Dynamic Programming; policy evaluation, policy improvement, policy iteration, and value iteration NEW

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Chapter 4 — Monte Carlo Methods

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Chapter 5 — Temporal-Difference Learning

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Chapter 6 — Q-Learning and SARSA

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Chapter 7 — Deep Reinforcement Learning

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Chapter 8 — Policy Gradient Methods

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Chapter 9 — Actor-Critic Methods

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Chapter 10 — Multi-Criteria Decision Making

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Chapter 11 — Uncertainty and Risk in Decision Making

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Chapter 12 — Fuzzy Decision Making

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Chapter 13 — Applications of Reinforcement Learning and Decision Making

Lecture Slides   |   Exercises

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Final — Reinforcement Learning and Decision-Making Project