Machine Learning. Machine Learning: Jordan Boyd-Graber University of Maryland REINFORCEMENT LEARNING. Slides adapted from Tom Mitchell and Peter Abeel

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1 Machine Learning Machine Learning: Jordan Boyd-Graber University of Maryland REINFORCEMENT LEARNING Slides adapted from Tom Mitchell and Peter Abeel Machine Learning: Jordan Boyd-Graber UMD Machine Learning 1 / 24

2 Control Learning Control Learning Consider learning to choose actions, e.g., Roomba learning to dock on battery charger Learning to choose actions to optimize factory output Learning to play Backgammon Note several problem characteristics: Delayed reward Opportunity for active exploration Possibility that state only partially observable Possible need to learn multiple tasks with same sensors/effectors Machine Learning: Jordan Boyd-Graber UMD Machine Learning 2 / 24

3 Control Learning One Example: TD-Gammon Learn to play Backgammon Immediate reward +1 if win -1 if lose for all other states [Tesauro, 1995] Trained by playing 1.5 million games against itself Now approximately equal to best human player Machine Learning: Jordan Boyd-Graber UMD Machine Learning 3 / 24

Control Learning Reinforcement Learning Problem At each step t the agent: Executes action a t Receives observation o t Receives scalar reward r t The

4 Control Learning Reinforcement Learning Problem At each step t the agent: Executes action a t Receives observation o t Receives scalar reward r t The environment: Receives action a t Emits observation o t +1 Emits scalar reward r t +1 Machine Learning: Jordan Boyd-Graber UMD Machine Learning 4 / 24

5 Control Learning Reinforcement Learning Problem Agent State Reward Action Environment s a r s 1 a 1 r 1 s 2 a 2 r 2... Machine Learning: Jordan Boyd-Graber UMD Machine Learning 5 / 24

6 Control Learning Markov Decision Processes Assume finite set of states S set of actions A at each discrete time agent observes state st S and chooses action a t A then receives immediate reward rt and state changes to st +1 Markov assumption: st +1 = δ(s t,a t ) and r t = r (s t,a t ) i.e., r t and s t +1 depend only on current state and action functions δ and r may be nondeterministic functions δ and r not necessarily known to agent Machine Learning: Jordan Boyd-Graber UMD Machine Learning 6 / 24

7 Control Learning State Experience is a sequence of observations, actions, rewards o 1, r 1,a 1,...,a t 1,o t, r t (1) The state is a summary of experience s t = f (o 1, r 1,a 1,...,a t 1,o t, r t ) (2) In a fully observed environment s t = f (o t ) (3) Machine Learning: Jordan Boyd-Graber UMD Machine Learning 7 / 24

8 Control Learning Agent s Learning Task Execute actions in environment, observe results, and learn action policy π : S A that maximizes r t + γr t +1 + γ 2 r t from any starting state in S here γ < 1 is the discount factor for future rewards Note something new: Target function is π : S A but we have no training examples of form s,a training examples are of form s,a, r Machine Learning: Jordan Boyd-Graber UMD Machine Learning 8 / 24

9 Control Learning What makes an RL agent? Policy: agent s behaviour function Value function: how good is each state and/or action Model: agent s representation of the environment Machine Learning: Jordan Boyd-Graber UMD Machine Learning 9 / 24

10 Control Learning Policy A policy is the agent s behavior It is a map from state to action: Deterministic policy: a = π(s ) Stochastic policy: π(a s ) = p (a s ) Machine Learning: Jordan Boyd-Graber UMD Machine Learning 1 / 24

11 Value Function To begin, consider deterministic worlds... For each possible policy π the agent might adopt, we can define an evaluation function over states V π (s ) r t + γr t +1 + γ 2 r t γ i r t +i where r t, r t +1,... are from following policy π starting at state s i = Machine Learning: Jordan Boyd-Graber UMD Machine Learning 11 / 24

12 Q -learning Restated, the task is to learn the optimal policy π π argmax π V π (s ),( s ) r (s,a ) (immediate reward) values 1 1 G Q (s,a ) values One optimal policy Machine Learning: Jordan Boyd-Graber UMD Machine Learning 12 / 24

13 Q -learning Restated, the task is to learn the optimal policy π π argmax π V π (s ),( s ) r (s,a ) (immediate reward) values Q (s,a ) values G One optimal policy Machine Learning: Jordan Boyd-Graber UMD Machine Learning 12 / 24

14 Q -learning Restated, the task is to learn the optimal policy π π argmax π V π (s ),( s ) r (s,a ) (immediate reward) values Q (s,a ) values One optimal policy G Machine Learning: Jordan Boyd-Graber UMD Machine Learning 12 / 24

15 What to Learn We might try to have agent learn the evaluation function V π (which we write as V ) It could then do a lookahead search to choose best action from any state s because A problem: π (s ) = argmax a [r (s,a ) + γv (δ(s,a ))] This works well if agent knows δ : S A S, and r : S A R But when it doesn t, it can t choose actions this way Machine Learning: Jordan Boyd-Graber UMD Machine Learning 13 / 24

16 Q Function Define new function very similar to V Q (s,a ) r (s,a ) + γv (δ(s,a )) If agent learns Q, it can choose optimal action even without knowing δ! π (s ) = argmax a [r (s,a ) + γv (δ(s,a ))] π (s ) = argmaxq (s,a ) a Q is the evaluation function the agent will learn Machine Learning: Jordan Boyd-Graber UMD Machine Learning 14 / 24

17 Training Rule to Learn Q Note Q and V closely related: V (s ) = max a Q (s,a ) Which allows us to write Q recursively as Q (s t,a t ) = r (s t,a t ) + γv (δ(s t,a t ))) = r (s t,a t ) + γmax a Q (s t +1,a ) Nice! Let ˆQ denote learner s current approximation to Q. Consider training rule ˆQ (s,a ) r + γmax a ˆQ (s,a ) where s is the state resulting from applying action a in state s Machine Learning: Jordan Boyd-Graber UMD Machine Learning 15 / 24

18 Value Function A value function is a prediction of future reward: How much reward will I get from action a in state s? Q -value function gives expected total reward from state s and action a under policy π with discount factor γ (future rewards mean less than immediate) Q π (s,a ) = r t +1 + γr t +2 + γ 2 r t s,a (4) Machine Learning: Jordan Boyd-Graber UMD Machine Learning 16 / 24

19 A Value Function is Great! An optimal value function is the maximum achievable value Q (s,a ) = max π Q π (s,a ) = Q π (s,a ) (5) If you know the value function, you can derive policy π = argmaxq (s,a ) (6) a Machine Learning: Jordan Boyd-Graber UMD Machine Learning 17 / 24

20 Q Learning for Deterministic Worlds For each s,a initialize table entry ˆQ (s,a ) Observe current state s Do forever: Select an action a and execute it Receive immediate reward r Observe the new state s Update the table entry for ˆQ (s,a ) as follows: s s ˆQ (s,a ) r + γmax a ˆQ (s,a ) Machine Learning: Jordan Boyd-Graber UMD Machine Learning 18 / 24

21 Updating ˆQ R R 1 81 a right Initial state: s 1 Next state: s 2 ˆQ (s 1,a r i g h t ) r + γmax a ˆQ (s 2,a ) if rewards non-negative, then +.9 max{63,81,1} = 9 ( s,a,n) ˆQ n+1 (s,a ) ˆQ n (s,a ) and ( s,a,n) ˆQ n (s,a ) Q (s,a ) ˆQ converges to Q. Machine Learning: Jordan Boyd-Graber UMD Machine Learning 19 / 24

22 Nondeterministic Case What if reward and next state are non-deterministic? We redefine V,Q by taking expected values V π (s ) E [r t + γr t +1 + γ 2 r t ] E [ γ i r t +i ] i = Q (s,a ) E [r (s,a ) + γv (δ(s,a ))] Machine Learning: Jordan Boyd-Graber UMD Machine Learning 2 / 24

23 Nondeterministic Case Q learning generalizes to nondeterministic worlds Alter training rule to ˆQ n (s,a ) (1 α n ) ˆQ n 1 (s,a ) + α n [r + max a ˆQ n 1 (s,a )] where 1 α n = 1 + visits n (s,a ) Can still prove convergence of ˆQ to Q [Watkins and Dayan, 1992] Machine Learning: Jordan Boyd-Graber UMD Machine Learning 21 / 24

24 Temporal Difference Learning Q learning: reduce discrepancy between successive Q estimates One step time difference: Why not two steps? Or n? Q (1) (s t,a t ) r t + γmax a Q (2) (s t,a t ) r t + γr t +1 + γ 2 max a ˆQ (s t +1,a ) ˆQ (s t +2,a ) Q (n) (s t,a t ) r t + γr t γ (n 1) r t +n 1 + γ n max a Blend all of these: ˆQ (s t +n,a ) Q λ (s t,a t ) (1 λ) Q (1) (s t,a t ) + λq (2) (s t,a t ) + λ 2 Q (3) (s t,a t ) + Machine Learning: Jordan Boyd-Graber UMD Machine Learning 22 / 24

25 Temporal Difference Learning Q λ (s t,a t ) (1 λ) Q (1) (s t,a t ) + λq (2) (s t,a t ) + λ 2 Q (3) (s t,a t ) + Equivalent expression: Q λ (s t,a t ) = r t + γ[ (1 λ)max a TD(λ) algorithm uses above training rule Sometimes converges faster than Q learning ˆQ (s t,a t ) +λ Q λ (s t +1,a t +1 )] converges for learning V for any λ 1 (Dayan, 1992) Tesauro s TD-Gammon uses this algorithm Machine Learning: Jordan Boyd-Graber UMD Machine Learning 23 / 24

26 What if the number of states is huge and/or structured? Let s say we discover that state is bad In Q learning, we know nothing about similar states Machine Learning: Jordan Boyd-Graber UMD Machine Learning 24 / 24

27 What if the number of states is huge and/or structured? Let s say we discover that state is bad In Q learning, we know nothing about similar states Solution: Feature-based Representation Distance to closest ghost Distance to closest dot Number of ghosts Is Pacman in a tunnel? Machine Learning: Jordan Boyd-Graber UMD Machine Learning 24 / 24

Lecture 25: Learning 4. Victor R. Lesser. CMPSCI 683 Fall 2010

Lecture 25: Learning 4. Victor R. Lesser. CMPSCI 683 Fall 2010 Lecture 25: Learning 4 Victor R. Lesser CMPSCI 683 Fall 2010 Final Exam Information Final EXAM on Th 12/16 at 4:00pm in Lederle Grad Res Ctr Rm A301 2 Hours but obviously you can leave early! Open Book