Stanford AA228/CS238 Decision Making Under Uncertainty I Policy Gradient Estimation & Optimization
Автор: Stanford Online
Загружено: 2024-11-21
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October 24, 2024
Amelia Hardy, PhD student, Stanford University: https://ameliahardy.github.io/
Kiana Jafari: https://profiles.stanford.edu/kiana
This lecture is from the Stanford graduate course AA228/CS238: Decision Making under Uncertainty
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.
Guest Lecture Slides: https://drive.google.com/file/d/1A8Wt...
View the course website: https://aa228.stanford.edu/
Enroll in the course: https://online.stanford.edu/courses/a...
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