Modeling Rationality: Toward Better Performance Against Unknown Agents in Sequential Games
提出一种理性一致的对手建模方法，利用不完全信息序贯博弈中理性行为在不同信息集间的关联，在观测历史不足时仍能准确推断对手策略，并通过启发式自适应降低计算成本，在网格世界和扑克游戏中验证了效果。
Opponent modeling is necessary for autonomous agents to capture the intents of others during strategic interactions. Most previous works assume that they can access enough interaction history to build the model. However, it may not be realistic. To solve this problem, we present a novel rationality-consistent opponent modeling (ROM) method for games with imperfect information. In our approach, a game-theoretical concept of consistence about rationality is proposed to take advantage of the characteristic of imperfect information sequential games that rational behavior at disjoint information sets is correlated through anticipated opponent's behavior. With the correlation between different information sets, agents could infer the opponents' strategies at information sets correlated to observed behavior. To exploit the correlation, ROM attempts to conduct reasoning from the opponent's perspective and rationalize its past behavior. In this way, ROM acquires the ability to better adapt to different opponents and achieves a more accurate opponent model with insufficient observation history, which is verified by experiments in different settings. A heuristic adaptation approach is also applied in ROM, which updates the opponent model in an online manner and significantly reduces the computation cost. We evaluate ROM in both a grid world game and a poker game. Compared with other opponent modeling methods, ROM shows better performance and has more accurate predictions in both games against different types of opponents with limited action interactions. Experimental results also show that ROM's time cost is significantly reduced through heuristic adaptation.