# algorithmsbook

**Repository Path**: lee_zr/algorithmsbook

## Basic Information

- **Project Name**: algorithmsbook
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- **Created**: 2020-12-07
- **Last Updated**: 2020-12-19

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## README

# 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](https://web.stanford.edu/group/sisl/public/dm/chapter-1.pdf)

### Part I: Probabilistic Reasoning

2. [Representation](https://web.stanford.edu/group/sisl/public/dm/chapter-2.pdf)
3. [Inference](https://web.stanford.edu/group/sisl/public/dm/chapter-3.pdf)
4. [Parameter Learning](https://web.stanford.edu/group/sisl/public/dm/chapter-4.pdf)
5. [Structure Learning](https://web.stanford.edu/group/sisl/public/dm/chapter-5.pdf)
6. [Simple Decisions](https://web.stanford.edu/group/sisl/public/dm/chapter-6.pdf)

### Part II: Sequential Problems

7. [Exact Solution Methods](https://web.stanford.edu/group/sisl/public/dm/chapter-7.pdf)
8. [Approximate Value Functions](https://web.stanford.edu/group/sisl/public/dm/chapter-8.pdf)
9. [Online Planning](https://web.stanford.edu/group/sisl/public/dm/chapter-9.pdf)
10. [Policy Search](https://web.stanford.edu/group/sisl/public/dm/chapter-10.pdf)
11. [Policy Gradient Estimation](https://web.stanford.edu/group/sisl/public/dm/chapter-11.pdf)
12. [Policy Gradient Optimization](https://web.stanford.edu/group/sisl/public/dm/chapter-12.pdf)
13. [Actor-Critic Methods](https://web.stanford.edu/group/sisl/public/dm/chapter-13.pdf)
14. [Policy Validation](https://web.stanford.edu/group/sisl/public/dm/chapter-14.pdf)

### Part III: Model Uncertainty

15. [Exploration and Exploitation](https://web.stanford.edu/group/sisl/public/dm/chapter-15.pdf)
16. [Model-Based Methods](https://web.stanford.edu/group/sisl/public/dm/chapter-16.pdf)
17. [Model-Free Methods](https://web.stanford.edu/group/sisl/public/dm/chapter-17.pdf)
18. Imitation Learning

### Part IV: State Uncertainty

19. [Beliefs](https://web.stanford.edu/group/sisl/public/dm/chapter-19.pdf)
20. [Exact Belief State Planning](https://web.stanford.edu/group/sisl/public/dm/chapter-20.pdf)
21. [Offline Belief State Planning](https://web.stanford.edu/group/sisl/public/dm/chapter-21.pdf)
22. [Online Belief State Planning](https://web.stanford.edu/group/sisl/public/dm/chapter-22.pdf)
23. [Controller Abstractions](https://web.stanford.edu/group/sisl/public/dm/chapter-23.pdf)

### Part V: Multiagent Systems

24. [Multiagent Reasoning](https://web.stanford.edu/group/sisl/public/dm/chapter-24.pdf)
25. Sequential Problems
26. State Uncertainty
27. Collaborative Agents

### Appendices

28. A: [Mathematical Concepts](https://web.stanford.edu/group/sisl/public/dm/chapter-28.pdf)
29. B: [Probability Distributions](https://web.stanford.edu/group/sisl/public/dm/chapter-29.pdf)
30. C: [Computational Complexity](https://web.stanford.edu/group/sisl/public/dm/chapter-30.pdf)
31. D: [Neural Representations](https://web.stanford.edu/group/sisl/public/dm/chapter-31.pdf)
32. E: [Search Algorithms](https://web.stanford.edu/group/sisl/public/dm/chapter-32.pdf)
33. F: [Problems](https://web.stanford.edu/group/sisl/public/dm/chapter-33.pdf)
34. G: [Julia](https://web.stanford.edu/group/sisl/public/dm/chapter-34.pdf)