Algorithms for Decision Making

Mykel Kochenderfer, Tim Wheeler, and Kyle Wray

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.

Draft chapters will be released gradually for feedback. Please file issues for suggestions and comments (or email the address listed at the bottom of the pages of the PDF).

We are interested in all forms of feedback including, but not limited to:

1. Errors
2. Improvements to code (especially improvements for clarity over speed)
3. Typos
4. Areas that are confusing
5. Critical topics that are missing
6. Ideas for examples or exercises

Outline

1. Introduction

Part I: Probabilistic Reasoning

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

Part II: Sequential Problems

7. Exact Solution Methods
8. Approximate Value Functions
9. Online Planning
10. Policy Search
11. Policy Gradient Estimation
12. Policy Gradient Optimization
13. Actor-Critic Methods
14. Policy Validation

Part III: Model Uncertainty

15. Exploration and Exploitation
16. Model-Based Methods
17. Model-Free Methods
18. Imitation Learning

Part IV: State Uncertainty

19. Beliefs
20. Exact Belief State Planning
21. Offline Belief State Planning
22. Online Belief State Planning
23. Controller Abstractions

Part V: Multiagent Systems

24. Multiagent Reasoning
25. Sequential Problems
26. State Uncertainty
27. Collaborative Agents

Appendices

28. A: Mathematical Concepts
29. B: Probability Distributions
30. C: Computational Complexity
31. D: Neural Representations
32. E: Search Algorithms
33. F: Problems
34. G: Julia