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Monte Carlo Tree Search: Insights and Applications BCS Real AI Event Simon Lucas Game Intelligence Group University of Essex.

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Presentation on theme: "Monte Carlo Tree Search: Insights and Applications BCS Real AI Event Simon Lucas Game Intelligence Group University of Essex."— Presentation transcript:

1 Monte Carlo Tree Search: Insights and Applications BCS Real AI Event Simon Lucas Game Intelligence Group University of Essex

2 Outline General machine intelligence: the ingredients Monte Carlo Tree Search – A quick overview and tutorial Example application: Mapello – Note: Game AI is Real AI !!! Example test problem: Physical TSP Results of open competitions Challenges and future directions

3 General Machine Intelligence: the ingredients Evolution Reinforcement Learning Function approximation – Neural nets, N-Tuples etc Selective search / Sample based planning / Monte Carlo Tree Search

4 Conventional Game Tree Search Minimax with alpha-beta pruning, transposition tables Works well when: – A good heuristic value function is known – The branching factor is modest E.g. Chess: Deep Blue, Rybka – Super-human on a smartphone! Tree grows exponentially with search depth

5 Go Much tougher for computers High branching factor No good heuristic value function MCTS to the rescue! “Although progress has been steady, it will take many decades of research and development before world-championship– calibre go programs exist”. Jonathan Schaeffer, 2001

6 Monte Carlo Tree Search (MCTS) Upper Confidence bounds for Trees (UCT) Further reading:

7 Attractive Features Anytime Scalable – Tackle complex games and planning problems better than before – May be logarithmically better with increased CPU No need for heuristic function – Though usually better with one Next we’ll look at: – General MCTS – UCT in particular

8 MCTS: the main idea Tree policy: choose which node to expand (not necessarily leaf of tree) Default (simulation) policy: random playout until end of game

9 MCTS Algorithm Decompose into 6 parts: MCTS main algorithm – Tree policy Expand Best Child (UCT Formula) – Default Policy – Back-propagate We’ll run through these then show demos

10 MCTS Main Algorithm BestChild simply picks best child node of root according to some criteria: e.g. best mean value In our pseudo-code BestChild is called from TreePolicy and from MctsSearch, but different versions can be used – E.g. final selection can be the max value child or the most frequently visited one

11 TreePolicy Note that node selected for expansion does not need to be a leaf of the tree But it must have at least one untried action

12 Expand

13 Best Child (UCT) This is the standard UCT equation – Used in the tree Higher values of c lead to more exploration Other terms can be added, and usually are – More on this later

14 DefaultPolicy Each time a new node is added to the tree, the default policy randomly rolls out from the current state until a terminal state of the game is reached The standard is to do this uniformly randomly – But better performance may be obtained by biasing with knowledge

15 Backup Note that v is the new node added to the tree by the tree policy Back up the values from the added node up the tree to the root

16 MCTS Builds Asymmetric Trees (demo)

17 All Moves As First (AMAF), Rapid Value Action Estimates (RAVE) Additional term in UCT equation: – Treat actions / moves the same independently of where they occur in the move sequence

18 Using for a new problem: Implement the State interface

19 Example Application: Mapello

20 Othello Each move you must Pincer one or more opponent counters between the one you place and an existing one of your colour Pincered counters are flipped to your own colour Winner is player with most pieces at the end

21 Basics of Good Game Design Simple rules Balance Sense of drama Outcome should not be obvious

22 Othello Example – white leads: -58 (from )http://radagast.se/othello/Help/strategy.html

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26 Black wins with score of 16

27 Mapello Take the counter-flipping drama of Othello Apply it to novel situations – Obstacles – Power-ups (e.g. triple square score) – Large maps with power-plays e.g. line fill Novel games – Allow users to design maps that they are expert in – The map design is part of the game Research bonus: large set of games to experiment with

28 Example Initial Maps

29 Or how about this?

30 Need Rapidly Smart AI Give players a challenging game – Even when the game map can be new each time Obvious easy to apply approaches – TD Learning – Monte Carlo Tree Search (MCTS – Combinations of these … E.g. Silver et al, ICML 2008 Robles et al, CIG 2011

31 MCTS (see Browne et al, TCIAIG 2012) Simple algorithm Anytime No need for a heuristic value function E-E balance Works well across a range of problems

32 Demo TDL learns reasonable weights rapidly How well will this play at 1 ply versus limited toll-out MCTS?

33 For Strong Play … Combine MCTS, TDL, N-Tuples

34 Where to play / buy Coming to Android (November 2012) Nestorgames (http://www.nestorgames.com)http://www.nestorgames.com

35 MCTS in Real-Time Games: PTSP Hard to get long-term planning without good heuristics

36 Optimal TSP order != PTSP Order 36

37 MCTS: Challenges and Future Directions Better handling of problems with continuous action spaces – Some work already done on this Better understanding of handling real-time problems – Use of approximations and macro-actions Stochastic and partially observable problems / games of incomplete and imperfect information Hybridisation: – with evolution – with other tree search algorithms

38 Conclusions MCTS: a major new approach to AI Works well across a range of problems – Good performance even with vanilla UCT – Best performance requires tuning and heuristics – Sometimes the UCT formula is modified or discarded Can be used in conjunction with RL – Self tuning And with evolution – E.g. evolving macro-actions

39 Further reading and links


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