Lucas, SM and Samothrakis, S and Pérez, D (2014) Fast evolutionary adaptation for Monte Carlo Tree Search. In: UNSPECIFIED, ? - ?.

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Abstract

© Springer-Verlag Berlin Heidelberg 2014. This paper describes a new adaptive Monte Carlo Tree Search (MCTS) algorithm that uses evolution to rapidly optimise its performance. An evolutionary algorithm is used as a source of control parameters to modify the behaviour of each iteration (i.e. each simulation or roll-out) of the MCTS algorithm; in this paper we largely restrict this to modifying the behaviour of the random default policy, though it can also be applied to modify the tree policy.This method of tightly integrating evolution into the MCTS algorithm means that evolutionary adaptation occurs on a much faster time-scale than has previously been achieved, and addresses a particular problem with MCTS which frequently occurs in real-time video and control problems: that uniform random roll-outs may be uninformative.Results are presented on the classicMountain Car reinforcement learning benchmark and also on a simplified version of Space Invaders. The results clearly demonstrate the value of the approach, significantly outperforming “standard” MCTS in each case. Furthermore, the adaptation is almost immediate, with no perceptual delay as the system learns: the agent frequently performs well from its very first game.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User:	Jim Jamieson
Date Deposited:	04 Dec 2014 13:31
Last Modified:	23 Jan 2019 02:15
URI:	http://repository.essex.ac.uk/id/eprint/11981

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