Approximate Value Functions

Probability

Structure Learning

Policy Search

Beliefs

Multiagent Reasoning

Inference

Exact Solutions

Actor Critic

Offline Belief State Planning

Parameter Learning

Online Planning

Model-Based and Model-Free Reinforcement Learning

Controller Abstractions

Simple Decisions

Policy Gradient Estimation and Optimization

Exact Belief State Planning

Policy Validation, Implications, and Summary

Representations

Bayesian Networks Representation and Inference

Exploration / Exploitation

Online Belief State Planning

AA 228 / CS 238 Decision Making under Uncertainty Stanford University Winter 2023 The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language. This course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. - You will gain a broad fundamental understanding of the mathematical models and solution methods for decision making (exercises, three quizzes).
- You will be able to implement and extend key algorithms for learning and decision making (two programming projects).
- You will be able to identify an application of the theory in this course and formulate it mathematically (proposal).
- You will gain a deep understanding of an area of particular interest and apply it to a problem (final project).
- You will be able to critique approaches to solving decision problems (peer review). basic probability and fluency in a high-level programming language. This year we will be using the textbook titled *Algorithms for Decision Making*, MIT Press, 2022. Printed copies are available for purchase or you may download it for free as a PDF. Details are [here](https://algorithmsbook.com/decisionmaking).

The textbook serves as the official lecture notes.

AA 228 / CS 238 Decision Making under Uncertainty

Artificial Intelligence

Approximate Value Functions

1 courses cover this concept

AA 228 / CS 238 Decision Making under Uncertainty