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Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search

2010; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-13800-3_9

1611-3349

Vincent Berthier, Hassen Doghmen, Olivier Teytaud,

Evolutionary Algorithms and Applications

Monte-Carlo Tree Search algorithms (MCTS [4,6]), including upper confidence trees (UCT [9]), are known for their impressive ability in high dimensional control problems. Whilst the main testbed is the game of Go, there are increasingly many applications [13,12,7]; these algorithms are now widely accepted as strong candidates for high-dimensional control applications. Unfortunately, it is known that for optimal performance on a given problem, MCTS requires some tuning; this tuning is often handcrafted or automated, with in some cases a loss of consistency, i.e. a bad behavior asymptotically in the computational power. This highly undesirable property led to a stupid behavior of our main MCTS program MoGo in a real-world situation described in section 3. This is a big trouble for our several works on automatic parameter tuning [3] and the genetic programming of new features in MoGo. We will see in this paper: