Markov decision processes
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1 CS 2740 Knowledge representation Lecture 24 Markov decision processes Milos Hauskrecht 5329 Sennott Square Administrative announcements Final exam: Monday, December 8, 2008 In-class Only the material covered after the midterm Term project assignment: Thursday, December 11, 2008 noon
2 Decision trees Decision tree: A basic approach to represent decision making problems in the presence of uncertainty Limitations: What if there are many decision stages to consider? What if there are many different outcomes? Markov decision process (MDP) A framework for representing complex multi-stage decision problems in the presence of uncertainty More compact representation of the decision problem Efficient solutions Markov decision process Markov decision process (MDP) Models the dynamics of the environment under different actions Frequently used in AI, OR, control theory Markov assumption: next state depends on the previous state and action, and not states (actions) in the past action t-1 state t-1 state t reward t-1
3 Markov decision process action t-1 state t-1 state t reward t-1 Formal definition: 4-tuple ( S, A, T, R) A set of states S ( X ) locations of a robot A set of actions A move actions Transition model S A S [0,1] where can I get with different moves Reward model S A S R reward/cost for a transition Markov decision problem Example: agent navigation in the Maze 4 moves in compass directions Effects of moves are stochastic we may wind up in other than intended location with non-zero probability Objective: reach the goal state in the shortest expected time moves
4 Agent navigation example An MDP model: State: S position of an agent Action: A a move Transition model P Reward: R -1 for each move +100 for reaching the goal moves oal: find the policy maximizing future expected rewards Policy A policy: maps states to actions π : S A moves π : Position 1 Position 2 Position 20 right right left
5 MDP problem We want to find the best policy π : S A Value function ( V ) for a policy, quantifies the goodness of a policy through, e.g. infinite horizon, discounted model It: E ( =0 t γ t rt 1. combines future rewards over a trajectory 2. combines rewards for multiple trajectories (through expectation-based measures) ) Valuation models Objective: Find a policy π : S A That maximizes some combination of future reinforcements (rewards) received over time Valuation models (quantify how good the mapping is): Finite horizon model E ( T r t t = 0 Infinite horizon discounted model = 0 t Average reward T 1 lim E ( rt ) T T ) E ( γ t rt ) Discount factor: 0 < γ < 1 t = 0 Time horizon: T > 0
6 Value of a policy for MDP Assume a fixed policy π : S A How to compute the value of a policy under infinite horizon discounted model? Fixed point equation: V π ( s ) = R ( s, π ( s )) expected one step reward for the first action + γ For a finite state space we get a set of linear equations s ' S P ( s ' s, π ( s )) V π ( s ' ) expected discounted reward for following the policy for the rest of the steps 1 v = r + Uv v = ( I U) r Optimal policy The value of the optimal policy V ( s) = max R( s, + γ P( s' s, V ( s') a A s' S expected one step reward for the first action expected discounted reward for following the opt. policy for the rest of the steps Value function mapping form: V ( s ) = ( HV )( s ) The optimal policy: π : S A π ( s) = arg max Rsa (, ) + γ Ps ( ' sav, ) ( s') a A s' S
7 Computing optimal policy Three methods: Value iteration Policy iteration Linear program solutions Value iteration Method: computes the optimal value function first then the policy iterative approximation converges to the optimal value function Value iteration ( ε ) initialize V ;; V is vector of values for all states repeat set V' V set V HV until V' V ε output π ( s) = arg max R ( s, + γ P( s' s, V ( s' ) a A s ' S
8 Policy iteration Method: Takes a policy and computes its value Iteratively improves the policy, till policy cannot be further improved Policy iteration initialize a policy µ repeat set π µ compute π V compute µ ( s) = arg max R( s, + γ P ( s' s, V a A s ' S until π µ output π π ( s' ) Linear program solution Method: converts the problem as a linear program problem Let v s denotes V(s) Linear program: Subject to: v max v s s S R( s, + γ P ( s' s, s v s ' s ' S s S, a A
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