The Dynamic Programming Algorithm
- Introduction
- The Basic Problem
- The Dynamic Programming Algorithm
- State Augmentation and Other Reformulations
- Some Mathematical Issues
- Dynamic Programming and Minimax Control
- Notes, Sources, and Exercises
Deterministic Systems and the Shortest Path
Problem
- Finite-State Systems and Shortest Paths
- Some Shortest Path Applications
- Critical Path Analysis
- Hidden Markov Models and the Viterbi Algorithm
- Shortest Path Algorithms
- Label Correcting Methods - A* Algorithm
- Branch-and-Bound
- Constrained and Multiobjective Problems
- Notes, Sources, and Exercises
Problems with Perfect State Information
- Linear Systems and Quadratic Cost
- Inventory Control
- Dynamic Portfolio Analysis
- Optimal Stopping Problems
- Scheduling and the Interchange Argument
- Set-Membership Description of Uncertainty
- Set-Membership Estimation
- Control with Unknown-but-Bounded Disturbances
- Notes, Sources, and Exercises
Problems with Imperfect State
Information
- Reductions to the Perfect Information Case
- Linear Systems and Quadratic Cost
- Minimum Variance Control of Linear Systems
- Sufficient Statistics and Finite-State Markov Chains
- Sequential Hypothesis Testing
- Notes, Sources, and Exercises
Introduction to Infinite Horizon
Problems
- An Overview
- Stochastic Shortest Path Problems
- Discounted Problems
- Average Cost Problems
- Semi-Markov Problems
- Notes, Sources, and Exercises
Approximate Dynamic Programming
- Cost Approximation and Limited Lookahead
- Error Bounds and Cost Improvement
- Computation of Suboptimal Policies - Stochastic Programming
- Problem Approximation
- Enforced Decomposition
- Probabilistic Approximation - Certainty Equivalent Control
- Aggregation
- Parametric Cost Approximation
- Feature-Based Architectures and Neural Networks
- Sequential Dynamic Programming Approximation
- Q-Factor Parametric Approximation
- Parametric Approximation in Infinite Horizon Problems
- Computer Chess
- On-Line Approximation and Optimization
- Rollout Algorithms
- Rollout for Discrete Deterministic Problems
- Model Predictive Control
- Open-Loop Feedback Control
- Simulation-Based Cost-to-go Approximation
- Stochastic Rollout and Monte Carlo Tree Search
- Variance Reduction in Rollout
- Approximation in Policy Space
- Adaptive Control
- Discretization Issues
- Notes, Sources, and Exercises
Deterministic Continuous-Time Optimal
Control
- Continuous-Time Optimal Control
- The Hamilton-Jacobi-Bellman Equation
- The Pontryagin Minimum Principle
- An Informal Derivation Using the HJB Equation
- A Derivation Based on Variational Ideas
- The Minimum Principle for Discrete-Time Problems
- Extensions of the Minimum Principle
- Fixed Terminal State
- Free Initial State
- Free Terminal Time
- Time-Varying System and Cost
- Singular Problems
- Notes, Sources, and Exercises
Appendix A: Mathematical Review
Appendix B: On Optimization Theory
Appendix C: On Probability Theory
Appendix D: On Finite-State Markov Chains
Appendix E: Kalman Filtering
Appendix F: Formulating Problems of Decision Under
Uncertainty