GAIG Game AI Research Group @ QMUL

Self-adaptive MCTS for General Video Game Playing

2018
Chiara F. Sironi and Jialin Liu and Diego Perez-Liebana and Raluca D. Gaina and Ivan Bravi and Simon M. Lucas and Mark H. M. Winands

Abstract

Monte-Carlo Tree Search (MCTS) has shown particular success in General Game Playing (GGP) and General Video Game Playing (GVGP) and many enhancements and variants have been developed. Recently, an on-line adaptive parameter tuning mechanism for MCTS agents has been proposed that almost achieves the same performance as off-line tuning in GGP. In this paper we apply the same approach to GVGP and use the popular General Video Game AI (GVGAI) framework, in which the time allowed to make a decision is only 40ms. We design three Self-Adaptive MCTS (SA-MCTS) agents that optimize on-line the parameters of a standard non-Self-Adaptive MCTS agent of GVGAI. The three agents select the parameter values using Naïve Monte-Carlo, an Evolutionary Algorithm and an N-Tuple Bandit Evolutionary Algorithm respectively, and are tested on 20 single-player games of GVGAI. The SA-MCTS agents achieve more robust results on the tested games. With the same time setting, they perform similarly to the baseline standard MCTS agent in the games for which the baseline agent performs well, and significantly improve the win rate in the games for which the baseline agent performs poorly. As validation, we also test the performance of non-Self-Adaptive MCTS instances that use the most sampled parameter settings during the on-line tuning of each of the three SA-MCTS agents for each game. Results show that these parameter settings improve the win rate on the games Wait for Breakfast and Escape by 4 times and 150 times, respectively.
URL: https://link.springer.com/chapter/10.1007/978-3-319-77538-8_25
ISBN: 978-3-319-77538-8

Cite this work

@inproceedings{sironi2018mctsGVGP,
author= {Chiara F. Sironi and Jialin Liu and Diego Perez-Liebana and Raluca D. Gaina and Ivan Bravi and Simon M. Lucas and Mark H. M. Winands},
title= {{Self-adaptive MCTS for General Video Game Playing}},
year= {2018},
booktitle= {{Applications of Evolutionary Computation}},
pages= {358--375},
editor= {Kevin Sim and Paul Kaufmann},
publisher= {Springer International Publishing},
address= {Cham},
url= {https://link.springer.com/chapter/10.1007/978-3-319-77538-8_25},
isbn= {978-3-319-77538-8},
abstract= {Monte-Carlo Tree Search (MCTS) has shown particular success in General Game Playing (GGP) and General Video Game Playing (GVGP) and many enhancements and variants have been developed. Recently, an on-line adaptive parameter tuning mechanism for MCTS agents has been proposed that almost achieves the same performance as off-line tuning in GGP. In this paper we apply the same approach to GVGP and use the popular General Video Game AI (GVGAI) framework, in which the time allowed to make a decision is only 40ms. We design three Self-Adaptive MCTS (SA-MCTS) agents that optimize on-line the parameters of a standard non-Self-Adaptive MCTS agent of GVGAI. The three agents select the parameter values using Naïve Monte-Carlo, an Evolutionary Algorithm and an N-Tuple Bandit Evolutionary Algorithm respectively, and are tested on 20 single-player games of GVGAI. The SA-MCTS agents achieve more robust results on the tested games. With the same time setting, they perform similarly to the baseline standard MCTS agent in the games for which the baseline agent performs well, and significantly improve the win rate in the games for which the baseline agent performs poorly. As validation, we also test the performance of non-Self-Adaptive MCTS instances that use the most sampled parameter settings during the on-line tuning of each of the three SA-MCTS agents for each game. Results show that these parameter settings improve the win rate on the games Wait for Breakfast and Escape by 4 times and 150 times, respectively.},
}

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