This book provides a broad introduction to algorithms for decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them.


The full book is available as a PDF. You can also download individual chapters. The PDF is shared under a under a Creative Commons CC-BY-NC-ND license.

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  1. Introduction

Part I: Probabilistic Reasoning

  1. Representation
  2. Inference
  3. Parameter Learning
  4. Structure Learning
  5. Simple Decisions

Part II: Sequential Problems

  1. Exact Solution Methods
  2. Approximate Value Functions
  3. Online Planning
  4. Policy Search
  5. Policy Gradient Estimation
  6. Policy Gradient Optimization
  7. Actor-Critic Methods
  8. Policy Validation

Part III: Model Uncertainty

  1. Exploration and Exploitation
  2. Model-Based Methods
  3. Model-Free Methods
  4. Imitation Learning

Part IV: State Uncertainty

  1. Beliefs
  2. Exact Belief State Planning
  3. Offline Belief State Planning
  4. Online Belief State Planning
  5. Controller Abstractions

Part V: Multiagent Systems

  1. Multiagent Reasoning
  2. Sequential Problems
  3. State Uncertainty
  4. Collaborative Agents


  1. A: Mathematical Concepts
  2. B: Probability Distributions
  3. C: Computational Complexity
  4. D: Neural Representations
  5. E: Search Algorithms
  6. F: Problems
  7. G: Julia


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