Monte-Carlo Tree Search

1. Monte-Carlo Tree Search Alan Fern

2. Introduction • Rollout does not guarantee optimality or near optimality • It only guarantees policy improvement (under certain conditions) • Theoretical Question:Can we develop Monte-Carlo methods that give us near optimal policies? • With computation that does NOT depend on number of states! • This was an open theoretical question until late 90’s. • Practical Question:Can we develop Monte-Carlo methods that improve smoothly and quickly with more computation time? • Multi-stage rollout can improve arbitrarily, but each stage adds a big increase in time per move.

3. … … … … … … … … Look-Ahead Trees • Rollout can be viewed as performing one level of search on top of the base policy • In deterministic games and search problems it is common to build a look-ahead tree at a state to select best action • Can we generalize this to general stochastic MDPs? • Sparse Sampling is one such algorithm • Strong theoretical guarantees of near optimality ak a1 a2 Maybe we should searchfor multiple levels. …

4. Online Planning with Look-Ahead Trees • At each state we encounter in the environment we build a look-ahead tree of depth hand use it to estimate optimal Q-values of each action • Select action with highest Q-value estimate • s = current state • Repeat • T = BuildLookAheadTree(s) ;; sparse sampling or UCT ;; tree provides Q-value estimates for root action • a = BestRootAction(T) ;; action with best Q-value • Execute action a in environment • s is the resulting state

5. Planning with Look-Ahead Trees s s a1 a1 a1 a1 a1 a1 a1 a1 a1 a1 a2 a2 a2 a2 a2 a2 a2 a2 a2 a2 a2 a1 Real worldstate/action sequence s … … … … … … … … Build look-ahead tree Build look-ahead tree s11 s11 … … s1w s1w s21 s21 … … s2w s2w R(s1w,a1) R(s1w,a1) R(s1w,a2) R(s1w,a2) R(s11,a2) R(s11,a2) R(s11,a1) R(s11,a1) R(s21,a1) R(s21,a1) R(s21,a2) R(s21,a2) R(s2w,a1) R(s2w,a1) R(s2w,a2) R(s2w,a2) ………… …………

6. ExpectimaxTree (depth 1) max node (max over actions) Alternate max &expectation s a b expectation node (weighted averageover states) … … s1 s2 sn-1 sn s1 s2 sn-1 sn States -- weighted by probability of occuring after taking ain s • After taking each action there is a distribution over next states (nature’s moves) • The value of an action depends on the immediate reward and weighted value of those states

7. Expectimax Tree (depth 2) max node (max over actions) Alternate max &expectation s a b expectation node (max over actions) … … a a b b …...... … … … … Max Nodes: value equal to max of values of child expectation nodes Expectation Nodes: value is weighted average of values of its children Compute values from bottom up (leaf values = 0). Select root action with best value.

8. Expectimax Tree (depth H) s a b k = # actions … … H a a b b …...... … … … … …...... # states n … … …...... …...... In general can grow tree to any horizon H Size depends on size of the state-space. Bad! Size of tree =

9. Sparse Sampling Tree (depth H) s a b k = # actions H a a b b …...... … …...... # samples w …...... …...... Replace expectation with average over w samples w will typically be much smaller than n. Size of tree =

10. Sparse Sampling [Kearns et. al. 2002] The Sparse Sampling algorithm computes root value via depth first expansion Return value estimate V*(s,h) of state s and estimated optimal action a* SparseSampleTree(s,h,w) If h == 0 Then Return [0, null] For each action a in s Q*(s,a,h) = 0 For i = 1 to w Simulate taking a in s resulting in si and reward ri [V*(si,h),a*] = SparseSample(si,h-1,w) Q*(s,a,h) = Q*(s,a,h) + ri+ β V*(si,h) Q*(s,a,h) = Q*(s,a,h) / w ;; estimate of Q*(s,a,h) V*(s,h) = maxa Q*(s,a,h) ;; estimate of V*(s,h) a* = argmaxa Q*(s,a,h) Return [V*(s,h), a*]

11. SparseSample(h=2,w=2) Alternate max &averaging s a Max Nodes: value equal to max of values of child expectation nodes Average Nodes: value is average of values of w sampled children Compute values from bottom up (leaf values = 0). Select root action with best value.

12. SparseSample(h=2,w=2) Alternate max &averaging s a 10 SparseSample(h=1,w=2) Max Nodes: value equal to max of values of child expectation nodes Average Nodes: value is average of values of w sampled children Compute values from bottom up (leaf values = 0). Select root action with best value.

13. SparseSample(h=2,w=2) Alternate max &averaging s a 0 10 SparseSample(h=1,w=2) SparseSample(h=1,w=2) Max Nodes: value equal to max of values of child expectation nodes Average Nodes: value is average of values of w sampled children Compute values from bottom up (leaf values = 0). Select root action with best value.

14. SparseSample(h=2,w=2) Alternate max &averaging s a 5 0 10 SparseSample(h=1,w=2) SparseSample(h=1,w=2) Max Nodes: value equal to max of values of child expectation nodes Average Nodes: value is average of values of w sampled children Compute values from bottom up (leaf values = 0). Select root action with best value.

15. SparseSample(h=2,w=2) Alternate max &averaging s b a 3 5 2 4 SparseSample(h=1,w=2) SparseSample(h=1,w=2) Select action ‘a’ since its value is larger than b’s. Max Nodes: value equal to max of values of child expectation nodes Average Nodes: value is average of values of w sampled children Compute values from bottom up (leaf values = 0). Select root action with best value.

16. Sparse Sampling (Cont’d) • For a given desired accuracy, how largeshould sampling width and depth be? • Answered: Kearns, Mansour, and Ng (1999) • Good news: can gives values for w and H to achieve policy arbitrarily close to optimal • Values are independent of state-space size! • First near-optimal general MDP planning algorithm whose runtime didn’t depend on size of state-space • Bad news: the theoretical values are typically still intractably large---also exponential in H • Exponential in H is the best we can do in general • In practice: use small Hand use heuristic at leaves

17. Sparse Sampling (Cont’d) • In practice: use small H& evaluate leaves with heuristic • For example, if we have a policy, leaves could be evaluated by estimating the policy value (i.e. average reward across runs of policy) a1 a2 … … …...... … … … … policysims

18. Uniform vs. Adaptive Tree Search • Sparse sampling wastes time on bad parts of tree • Devotes equal resources to each state encountered in the tree • Would like to focus on most promising parts of tree • But how to control exploration of new parts of tree vs. exploiting promising parts? • Need adaptive bandit algorithmthat explores more effectively

19. What now? • Adaptive Monte-Carlo Tree Search • UCB Bandit Algorithm • UCT Monte-Carlo Tree Search

20. Bandits: Cumulative Regret Objective • Problem: find arm-pulling strategy such that the expected total reward at time n is close to the best possible (one pull per time step) • Optimal (in expectation) is to pull optimal arm n times • UniformBandit is poor choice --- waste time on bad arms • Must balance exploring machines to find good payoffs and exploiting current knowledge s ak a1 a2 …

21. Cumulative Regret Objective • Protocol: At time step n the algorithm picks an arm based on what it has seen so far and receives reward ( are random variables). • Expected Cumulative Regret (: difference between optimal expected cumulative reward and expected cumulative reward of our strategy at time n

22. UCB Algorithm for Minimizing Cumulative Regret Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2), 235-256. • Q(a) : average reward for trying action a (in our single state s) so far • n(a) : number of pulls of arm a so far • Action choice by UCB after n pulls: • Assumes rewards in [0,1]. We can always normalize if we know max value.

23. UCB: Bounded Sub-Optimality Value Term:favors actions that looked good historically Exploration Term:actions get an exploration bonus that grows with ln(n) Expected number of pulls of sub-optimal arm a is bounded by: where is the sub-optimality of arm a Doesn’t waste much time on sub-optimal arms, unlike uniform!

24. UCB Performance Guarantee[Auer, Cesa-Bianchi, & Fischer, 2002] Theorem: The expected cumulative regret of UCB after n arm pulls is bounded by O(log n) • Is this good? Yes. The average per-step regret is O Theorem: No algorithm can achieve a better expected regret (up to constant factors)

25. What now? • Adaptive Monte-Carlo Tree Search • UCB Bandit Algorithm • UCT Monte-Carlo Tree Search

26. UCT Algorithm [Kocsis & Szepesvari, 2006] • UCT is an instance of Monte-Carlo Tree Search • Applies principle of UCB • Similar theoretical properties to sparse sampling • Much better anytime behavior than sparse sampling • Famous for yielding a major advance in computer Go • A growing number of success stories

27. Monte-Carlo Tree Search • Builds a sparse look-ahead tree rooted at current state by repeated Monte-Carlo simulation of a “rollout policy” • During construction each tree node s stores: • state-visitation count n(s) • action counts n(s,a) • action values Q(s,a) • Repeat until time is up • Execute rollout policy starting from root until horizon(generates a state-action-reward trajectory) • Add first node not in current tree to the tree • Update statistics of each tree nodes on trajectory • Increment n(s) and n(s,a) for selected action a • Update Q(s,a) by total reward observed after the node

28. Example MCTS • For illustration purposes we will assume MDP is: • Deterministic • Only non-zero rewards are at terminal/leaf nodes • Algorithm is well-defined without these assumpitons

29. Iteration 1 Current World State Initially tree is single leaf 1 1 new tree node Rollout Policy 1 Terminal(reward = 1) Assume all non-zero reward occurs at terminal nodes.

30. Iteration 2 Current World State 1 1 new tree node RolloutPolicy 0 Terminal(reward = 0)

31. Iteration 3 Current World State 1/2 1 0

32. Iteration 3 Current World State 1/2 Rollout Policy 1 0

33. Iteration 3 Current World State 1/2 RolloutPolicy 1 0 new tree node RolloutPolicy 0

34. Iteration 4 Current World State 1/3 RolloutPolicy 0 1/2 0

35. Iteration 4 Current World State 1/3 0 1/2 0 1

36. Current World State 2/3 RolloutPolicy 0 2/3 0 1 What should the rollout policy be?

37. Rollout Policies • Monte-Carlo Tree Search algorithms mainly differ on their choice of rollout policy • Rollout policies have two distinct phases • Tree policy: selects actions at nodes already in tree(each action must be selected at least once) • Default policy: selects actions after leaving tree • Key Idea: the tree policy can use statistics collected from previous trajectories to intelligently expand tree in most promising direction • Rather than uniformly explore actions at each node

38. UCT Algorithm [Kocsis & Szepesvari, 2006] • Basic UCT uses random default policy • In practice often use hand-coded or learned policy • Tree policy is based on UCB: • Q(s,a) : average reward received in trajectories so far after taking action a in state s • n(s,a) : number of times action a taken in s • n(s) : number of times state s encountered Theoretical constant that must be selected empirically in practice

39. When all node actions tried once, select action according to tree policy Current World State 1/3 a2 a1 Tree Policy 0 1/2 0 1

40. When all node actions tried once, select action according to tree policy Current World State 1/3 Tree Policy 0 1/2 0 1

41. When all node actions tried once, select action according to tree policy Current World State 1/3 Tree Policy 0 1/2 0 1 0

42. When all node actions tried once, select action according to tree policy Current World State 1/4 Tree Policy 0 1/3 1 0/1 0

43. When all node actions tried once, select action according to tree policy Current World State 1/4 Tree Policy 0 1/3 1 0/1 0 1

44. When all node actions tried once, select action according to tree policy Current World State 2/5 Tree Policy 1/2 1/3 1 1 0/1 0

45. UCT Recap • To select an action at a state s • Build a tree using N iterations of monte-carlo tree search • Default policy is uniform random • Tree policy is based on UCB rule • Select action that maximizes Q(s,a)(note that this final action selection does not take the exploration term into account, just the Q-value estimate) • The more simulations the more accurate

46. Some Successes • Computer Go • Klondike Solitaire (wins 40% of games) • General Game Playing Competition • Real-Time Strategy Games • Combinatorial Optimization • Crowd Sourcing • List is growing • Usually extend UCT is some ways

47. Practical Issues • Selecting K • There is no fixed rule • Experiment with different values in your domain • Rule of thumb – try values of K that are of the same order of magnitude as the reward signal you expect • The best value of K may depend on the number of iterations N • Usually a single value of K will work well for values of N that are large enough

48. Practical Issues • UCT can have trouble building deep trees when actions can result in a large # of possible next states • Each time we try action ‘a’ we get a new state (new leaf) and very rarely resample an existing leaf s a b

49. Practical Issues • UCT can have trouble building deep trees when actions can result in a large # of possible next states • Each time we try action ‘a’ we get a new state (new leaf) and very rarely resample an existing leaf s a b

50. Practical Issues • Degenerates into a depth 1 tree search • Solution: We have a “sparseness parameter” that controls how many new children of an action will be generated • Set sparseness to something like 5 or 10 thensee if smaller values hurt performance or not s a b ….. …..