CS788 Dialogue Management Systems Lecture #2: Markov Decision Processes

Transcription

1 CS788 Dialogue Management Systems Lecture #2: Markov Decision Processes Kee-Eung Kim KAIST EECS Department Computer Science Division

2 Markov Decision Processes (MDPs) A popular model for sequential decision problems; Specified by A set of possible world states s 2 S A set of possible actions a 2 A A real-valued reward function R(s) A transition model T(s, a, s ) given by P(s s,a) Assumes fully observable environment with a Markovian transition model: P(S t+1 =s S t =s t, A t =a t, S t-1 =s t-1, A t-1 =a t-1, ) = P(S t+1 =s S t =s t, A t =a t ) Example: Maze Robot

3 Finite MDP Example: Recycling Robot At each step, robot has to decide whether it should (1) actively search for a can, (2) wait for someone to bring it a can, or (3) go to home base and recharge. Searching is better but runs down the battery; if runs out of power while searching, has to be rescued (which is bad). Decisions made on basis of current energy level: high, low. Reward = number of cans collected

4 Recycling Robot MDP S = fhigh; lowg A(high) = fsearch; waitg A(low) = fsearch; wait; rechargeg R search = expected # of cans while searching R wait = expected # of cans while waiting R search > R wait

5 Value Functions The value of a state is the expected return starting from that state; depends on the agent s policy State-value function of policy ¼ " 1 # X V ¼ (s) = E ¼ [R t js t = s] = E ¼ k r t+k+1 js t = s k=0 The value of taking an action in a state under policy p is the expected return starting from that state, taking that action, and thereafter following p : Action-value function for policy ¼ " 1 # X Q ¼ (s; a) = E ¼ [R t js t = s; a t = a] = E ¼ k r t+k+1 js t = s; a t = a k=0

6 Bellman Equation for V p Basic Idea: Bellman equation V ¼ (s) = E ¼ [R t js t = s] " 1 # X = E ¼ k r t+k+1 js t = s k=0 = E ¼ "r t+1 + = X a = X a R t = r t+1 + r t r t+3 + = r t+1 + (r t+2 + r t+3 + ) = r t+1 + R t+1 # 1X k r t+k+2 js t = s k=0 ¼(s; a) X T (s; a; s 0 ) s 0 " " 1 ## X R(s; a; s 0 ) + E ¼ k r t+k+2 js t+1 = s 0 k=0 ¼(s; a) X T (s; a; s 0 ) R(s; a; s 0 ) + V ¼ (s 0 ) s 0

7 More on Bellman Equation V ¼ (s) = X a ¼(s; a) X T(s; a; s 0 ) [R(s; a; s 0 ) + V ¼ (s 0 )] s 0 In fact, it is a set of linear equations for each state s; is a unique solution to this system of equations V ¼ Backup diagrams V ¼ Q ¼

8 MDP Example: Gridworld Actions: north, south, east, west; deterministic If would take agent off the grid: no move but reward = 1 Other actions produce reward = 0, except actions that move agent out of special states A and B as shown State-value function for equiprobable random policy; = 0:9

9 MDP Example: Golf State is ball location Reward of 1 for each stroke until the ball is in the hole Actions: putt (use putter) driver (use driver) putt succeeds anywhere on the green Value of a state?

10 Optimal Value Functions For finite MDPs, policies can be partially ordered: ¼ < ¼ 0 if and only if V ¼ (s) V ¼0 (s) for all s 2 S There are always one or more policies that are better than or equal to all the others. These are the optimal policies. We denote them all p * Optimal policies share the same optimal state-value function: V (s) = max V ¼ (s) for all s 2 S ¼ Optimal policies also share the same optimal action-value function: Q (s; a) = max Q ¼ (s; a) for all s 2 S and a 2 A(s) ¼ Expected return for executing action a in state s and then following an optimal policy

11 Optimal Action-Value Function for Golf We can hit the ball farther with driver than with putter, but with less accuracy Q*(s, driver) gives the value or using driver first, then using whichever actions are best

12 Bellman Optimality Equation for V* The value of a state under an optimal policy must equal the expected return for the best action from that state: V (s) = max a2a(s) Q (s; a) The backup diagram: = max E[r t+1 + V (s t+1 )js t = s; a t = a] a2a(s) X = max T(s; a; s 0 )[R(s; a; s 0 ) + V (s 0 )] a2a(s) s 0 V is the unique solution to the system of nonlinear equations

13 Bellman Optimality Equation for Q* Q (s; a) = E[r t+1 + max Q (s t+1 ; a 0 )js t = s; a t = a] a 0 = X T (s; a; s 0 )[R(s; a; s 0 ) + max Q (s 0 ; a 0 )] a 0 s 0 The backup diagram: Q is the unique solution to the system of nonlinear equations

14 Policies: Solution to MDPs p: S! A p(s) : recommended action for state s Following a policy p: (1) Determine the current state s (2) Execute action p(s) (3) Goto step 1 Evaluating p: How good is a policy p in a state s? Total sum of the rewards obtained can be infinite Finite horizon vs. Infinite horizon Additive rewards vs. Discounted rewards V ¼ ([s 0 ;s 1 ;s 2 ;:::]) = R(s 0 ) +R(s 1 ) +R(s 2 ) +::: V ¼ ([s 0 ; s 1 ; s 2 ; : : :]) = R(s 0 ) + R(s 1 ) + 2 R(s 2 ) + : : : Optimal policy:

15 Optimal Policy from V* Any policy that is greedy with respect to policy Therefore, given optimal actions V Example: Gridworld V is an optimal, one-step-ahead search produces the long-term

16 Optimal Policy from Q* Q Given, the agent does not even have to do a one-stepahead search: ¼ (s) = argmax Q (s; a) a2a(s)

17 Solving the Bellman Optimality Equation Finding an optimal policy by solving the Bellman Optimality Equation requires the following: accurate knowledge of environment dynamics; we have enough space and time to do the computation; the Markov Property How much space and time do we need? Polynomial in number of states (via dynamic programming methods) BUT, number of states can be huge (e.g., backgammon has about 1020 states) We usually have to settle for approximations Many RL methods (next week) can be understood as approximately solving the Bellman Optimality Equation But for this lecture, we assume we can solve without approximation

18 Value Iteration Algorithm Suppose that we know the utility of optimal policy It should satisfy Bellman X equation: V ¼ (s) = R(s) + max T(s; a; s 0 )V ¼ (s 0 ) a s 0 Then the optimal " policy can be derived by # ¼ (s) = arg max Iterative algorithm for solving X the Bellman equation V i+1 (s) Ã R(s) + max T(s; a; s 0 )V i (s 0 ) [Bellman update] a s 0 As i! 1 a R(s) + X T(s; a; s 0 )V ¼ (s 0 ) s 0, V i+1 converges to the utility of optimal policy Proof sketch: (1) prove that it converges to a vector (2) prove that vector satisfies the Bellman equation

19 Value Iteration Example?

20 Policy Iteration Algorithm From value iteration, let p i+1 (s) be the policy from the i-th Bellman update: " # ¼ i+1 (s) Ã arg max a R(s) + X T(s; a; s 0 )V i (s 0 ) s 0 p i+1 can be seen as improvement to current guess of optimal policy from inaccurate guess of value V i of following policy p i Idea: improvement will be more accurate if V i is more accurate! Policy Iteration Policy Evaluation: given a policy p, calculate V i = V pi (solving a set of linear equations) Policy Improvement: Calculate a new MEU policy p i+1, using one-step look-ahead based on V i (See above) If policy evaluation can be done very quickly, policy iteration is typically faster than value iteration

21 Summary Markov decision processes (MDPs): a popular model for sequential decision making problems under uncertainty Strong assumption Polynomial time computation of an optimal policy using dynamic programming However, if state space is too large, dynamic programming is impractical Application to dialogue management State space? Action space? Transition probabilities? Rewards? Next week: we will look at RL solutions Read Singh, Litman, Kearns, and Walker, Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System, Journal of Artificial Intelligence Research, 2002