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Unformatted text preview: 16.410/413 Principles of Autonomy and Decision Making Lecture 22: Markov Decision Processes I Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology November 29, 2010 Frazzoli (MIT) Lecture 22: MDPs November 29, 2010 1 / 16 Assignments Readings Lecture notes [AIMA] Ch. 17.13. Frazzoli (MIT) Lecture 22: MDPs November 29, 2010 2 / 16 Outline 1 Markov Decision Processes Frazzoli (MIT) Lecture 22: MDPs November 29, 2010 3 / 16 From deterministic to stochastic planning problems A basic planning model for deterministic systems (e.g., graph/tree search algorithms, etc.) is : Planning Model (Transition system + goal) A (discrete, deterministic) feasible planning model is defined by A countable set of states S . A countable set of actions A . A transition relation →⊆ S × A × S . An initial state s 1 ∈ S . A set of goal states s G ⊂ S . We considered the case in which the transition relation is purely deterministic: if ( s , a , s ) are in relation, i.e., ( s , a , s ) ∈→ , or, more concisely, s a→ s , then taking action a from state s will always take the state to s . Can we extend this model to include (probabilistic) uncertainty in the transitions? Frazzoli (MIT) Lecture 22: MDPs November 29, 2010 4 / 16 Markov Decision Process Instead of a (deterministic) transition relation, let us define transition probabilities; also, let us introduce a reward (or cost) structure: Markov Decision Process (Stoch. transition system + reward) A Markov Decision Process (MDP) is defined by A countable set of states S . A countable set of actions A . A transition probability function T : S × A × S → R + . An initial state s ∈ S . A reward function R : S × A × S → R + . In other words: if action a is applied from state s , a transition to state s will occur with probability T ( s , a , s )....
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This note was uploaded on 12/26/2011 for the course SCIENCE 16.410 taught by Professor Prof.brianwilliams during the Fall '10 term at MIT.
 Fall '10
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