Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning | |
Zhentao Tang1,2; Yuanheng Zhu1,2; Dongbin Zhao1,2; Simon M. Lucas3 | |
刊名 | IEEE TRANSACTIONS ON GAMES
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2020 | |
期号 | Early Access页码:Early Access |
关键词 | Rolling horizon evolution opponent model reinforcement learning supervised learning fighting game |
ISSN号 | 2475-1502 |
英文摘要 | The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for 2-player video game AI: large action space, diverse styles of characters and abilities, and the real-time nature. We propose a novel algorithm that combines Rolling Horizon Evolution Algorithm (RHEA) with opponent model learning. The approach is readily applicable to any 2-player video game. In contrast to conventional RHEA, an opponent model is proposed and is optimized by supervised learning with cross-entropy and reinforcement learning with policy gradient and Q-learning respectively, based on history observations from opponent. The model is learned during the live gameplay. With the learned opponent model, the extended RHEA is able to make more realistic plans based on what the opponent is likely to do. This tends to lead to better results. We compared our approach directly with the bots from the FTGAIC 2018 competition, and found our method to significantly outperform all of them, for all three character. Furthermore, our proposed bot with the policy- gradient-based opponent model is the only one without using Monte-Carlo Tree Search (MCTS) among top five bots in the 2019 competition in which it achieved second place, while using much less domain knowledge than the winner. |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/45042] ![]() |
专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
通讯作者 | Dongbin Zhao |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.Department of Electronic Engineering and Computer Engineering (EECS), Queen Mary University of London, London E1 4NS, U.K |
推荐引用方式 GB/T 7714 | Zhentao Tang,Yuanheng Zhu,Dongbin Zhao,et al. Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning[J]. IEEE TRANSACTIONS ON GAMES,2020(Early Access):Early Access. |
APA | Zhentao Tang,Yuanheng Zhu,Dongbin Zhao,&Simon M. Lucas.(2020).Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning.IEEE TRANSACTIONS ON GAMES(Early Access),Early Access. |
MLA | Zhentao Tang,et al."Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning".IEEE TRANSACTIONS ON GAMES .Early Access(2020):Early Access. |
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