Algorithms for Decision Making is a broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.
Table of Contents
- Introduction
- Representation
- Inference
- Parameter Learning
- Structure Learning
- Simple Decisions
- Exact Solution Methods
- Approximate Value Functions
- Online Planning
- Policy Search
- Policy Gradient Estimation
- Policy Gradient Optimization
- Actor-Critic Methods
- Policy Validation
- Exploration and Exploitation
- Model-Based Methods
- Model-Free Methods
- Imitation Learning
- Beliefs
- Exact Belief State Planning
- Offline Belief State Planning
- Online Belief State Planning
- Controller Abstractions
- Multiagent Reasoning
- Sequential Problems
- State Uncertainty
- Collaborative Agents
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Author(s): Mykel J. Kochenderfer, Tim A. Wheeler and Kyle H. Wray
Publisher: The MIT Press
Published: August 16, 2022
Format(s): PDF
File size: 12.2 MB
Number of pages: 700
Download / View Link(s): PDF
Publisher: The MIT Press
Published: August 16, 2022
Format(s): PDF
File size: 12.2 MB
Number of pages: 700
Download / View Link(s): PDF