Transpositions and move groups in Monte Carlo tree search

Childs, Benjamin E.; Brodeur, J.H.; Kocsis, Levente

doi:10.1109/cig.2008.5035667

Cited by 56 publications

(51 citation statements)

References 5 publications

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“…One of the features of the DST is that it allows the usage of Transposition Tables (TT) [2]. TT is a technique used to optimize the search in tree search based algorithms.…”

Section: ) Transposition Tables In Dstmentioning

confidence: 99%

See 1 more Smart Citation

Online and offline learning in multi-objective Monte Carlo Tree Search

Pérez

Samothrakis

Lucas

2013

2013 IEEE Conference on Computational Inteligence in Games (CIG)

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Abstract-Multi-Objective optimization has traditionally been applied to manufacturing, engineering or finance, with little impact in games research. However, its application to this field of study may provide interesting results, especially for games that are complex or long enough that long-term planning is not trivial and/or a good level of play depends on balancing several strategies within the game. This paper proposes a new Multi-Objective algorithm based on Monte Carlo Tree Search (MCTS). The algorithm is tested in two different scenarios and its learning capabilities are measured in an online and offline fashion. Additionally, it is compared with a state of the art multi-objective evolutionary algorithm (NSGA-II) and with a previously published Multi-Objective MCTS algorithm. The results show that our proposed algorithm provides similar or better results than other techniques.

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“…One of the features of the DST is that it allows the usage of Transposition Tables (TT) [2]. TT is a technique used to optimize the search in tree search based algorithms.…”

Section: ) Transposition Tables In Dstmentioning

confidence: 99%

“…2 For the latter one, two versions with 50 and 100 individuals per population have been tried. Each individual's genome is a sequence of N = 100 actions to One initial result to note is that all algorithms but one are able to converge to the optimal, NSGA-II (in both versions) and Pareto-MO-MCTS.…”

Section: A Offline Learningmentioning

confidence: 99%

Online and offline learning in multi-objective Monte Carlo Tree Search

Pérez

Samothrakis

Lucas

2013

2013 IEEE Conference on Computational Inteligence in Games (CIG)

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“…Different ways to use a transposition table with UCT were investigated in [8]. UCT was also successfully applied to the single-player game SameGame [20].…”

Section: The Uct Algorithmmentioning

confidence: 99%

“…There are multiple ways to implement a transposition table with UCT [8]. We have chosen to store the results of the playout and the number of visits in the nodes and not in the edges between nodes.…”

Section: Uct Path Selectionmentioning

confidence: 99%

Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing

Méhat

Cazenave²

2010

IEEE Trans. Comput. Intell. AI Games

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Abstract-Monte-Carlo tree search has recently been very successful for game playing particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare Nested Monte-Carlo Search (NMC), Upper Confidence bounds for Trees (UCT-T), UCT with transposition tables (UCT+T) and a simple combination of NMC and UCT+T (MAX) on single-player games of the past GGP competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version.

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“…Abstracted action spaces have also been used, for instance, by Childs et al [15] (and recently revisited by Van Eyck et al [16]), who applied combined groups of actions when using UCT in the artificial game tree P-Game. A P-Game tree is a minimax tree where the winner is decided by a counting mechanism on the final state of the board.…”

Section: Introductionmentioning

confidence: 99%

Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions

Pérez

Powley

Whitehouse

et al. 2014

IEEE Trans. Comput. Intell. AI Games

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Abstract-This paper presents a number of approaches for solving a real-time game consisting of a ship that must visit a number of waypoints scattered around a two-dimensional maze full of obstacles. The game, the Physical Travelling Salesman Problem (PTSP), which featured in two IEEE conference competitions during 2012, provides a good balance between long-term planning (finding the optimal sequence of waypoints to visit), and short-term planning (driving the ship in the maze). This paper focuses on the algorithm that won both PTSP Competitions: it takes advantage of the physics of the game to calculate the optimal order of waypoints, and it employs Monte Carlo Tree Search (MCTS) to drive the ship. The algorithm uses repetitions of actions (macro-actions) to reduce the search space for navigation. Variations of this algorithm are presented and analysed, in order to understand the strength of each one of its constituents and to comprehend what makes such an approach the best controller found so far for the PTSP.

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Transpositions and move groups in Monte Carlo tree search

Cited by 56 publications

References 5 publications

Online and offline learning in multi-objective Monte Carlo Tree Search

Online and offline learning in multi-objective Monte Carlo Tree Search

Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing

Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions

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