Algorithms for Decision Making

A rigorous guide to decision-making under uncertainty

Algorithms for Decision Making brings together the mathematical foundations and algorithmic methods behind modern decision-support systems. Rather than treating uncertainty as a side issue, the book places it at the center: how to reason about it, how to model it, and how to make good choices when outcomes are only partially known.

Written by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, this is a serious technical textbook with a clear pedagogical structure. It begins with core ideas in probability and inference, then moves through learning, decision networks, and sequential decision problems before reaching policy search, gradient methods, and actor-critic approaches. The progression is deliberate, and the chapter organization makes the book useful both as a course text and as a reference for self-study.

What the book covers

• Probability and Bayesian reasoning
• Inference, parameter learning, and structure learning
• Simple and sequential decision problems
• Markov decision processes and planning
• Approximate value functions and online planning
• Policy search and policy gradient methods
• Actor-critic methods and related reinforcement learning techniques

Why it stands out

The emphasis on algorithms, not just concepts, gives the book real practical weight. It is especially appealing to readers who want to understand how decision-making systems are built, how uncertainty affects model design, and how planning and reinforcement learning fit into a wider mathematical framework. The inclusion of Julia implementations, figures, examples, and exercises makes the material easier to work through methodically.

Best suited to

This title will be most useful for advanced students, researchers, and practitioners in computer science, artificial intelligence, statistics, aerospace, operations research, and engineering who want a disciplined introduction to algorithmic decision making.

For readers building a deeper understanding of uncertainty-aware systems, this is a substantial and well-structured ebook to keep close at hand.