Book contents
- Frontmatter
- Contents
- 1 Introduction and Overview
- 2 Preface to the Planning Chapters
- 3 Deterministic Decision Processes
- 4 Markov Chains
- 5 Markov Decision Processes and Finite Horizon Dynamic Programming
- 6 Discounted Markov Decision Processes
- 7 Episodic Markov Decision Processes
- 8 Linear Programming Solutions
- 9 Preface to the Learning Chapters
- 10 Reinforcement Learning: Model Based
- 11 Reinforcement Learning: Model Free
- 12 Large State Spaces: Value Function Approximation
- 13 Large State Space: Policy Gradient Methods
- 14 Regret Minimization
- Appendix A Dynamic Programming
- Appendix B Ordinary Differential Equations
- Reference
- Index
3 - Deterministic Decision Processes
Published online by Cambridge University Press: 31 July 2026
Book contents
- Frontmatter
- Contents
- 1 Introduction and Overview
- 2 Preface to the Planning Chapters
- 3 Deterministic Decision Processes
- 4 Markov Chains
- 5 Markov Decision Processes and Finite Horizon Dynamic Programming
- 6 Discounted Markov Decision Processes
- 7 Episodic Markov Decision Processes
- 8 Linear Programming Solutions
- 9 Preface to the Learning Chapters
- 10 Reinforcement Learning: Model Based
- 11 Reinforcement Learning: Model Free
- 12 Large State Spaces: Value Function Approximation
- 13 Large State Space: Policy Gradient Methods
- 14 Regret Minimization
- Appendix A Dynamic Programming
- Appendix B Ordinary Differential Equations
- Reference
- Index
Summary
This chapter introduces optimal planning in deterministic sequential decision problems. The deterministic dynamic system framework is presented with state transitions, encompassing both finite discrete models and continuous systems. Two fundamental problem settings are covered: finite-horizon decision problems solved via backward dynamic programming, and average cost criteria. Classic graph-theoretic algorithms are developed including Bellman–Ford, Dijkstra’s algorithm and A* search. The chapter extends to continuous optimal control, presenting the linear quadratic regulator (LQR) and iterative LQR.
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- Information
- Reinforcement Learning Foundations , pp. 12 - 35Publisher: Cambridge University PressPrint publication year: 2026
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