Markov Decision Processes
mathematical model for sequential decision making under uncertainty
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Videos Mentioning Markov Decision Processes
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15
Lex Fridman
A mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. It assumes the current state contains all necessary information about the future.
Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 1: Course Overview
Stanford Online
A formulation of problems in a stochastic setting, related to discrete-time optimal control.
Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. Program
Stanford Online
A mathematical framework used for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. It's central to the lecture's discussion on optimal control with uncertainty.
Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based Methods
Stanford Online
The mathematical formalism used for reinforcement learning, involving states, actions, rewards, and transitions.
Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 14: Intro to IL and RL
Stanford Online
A mathematical framework used in reinforcement learning to formally define sequential decision-making problems, consisting of states, actions, transition dynamics, reward functions, and a discount factor.