Enhancing Policy Gradient Algorithms with Search in Imperfect Information Games

Enhancing Policy Gradient Algorithms with Search in Imperfect Information Games

Ondřej Kubíček

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 8498-8499. https://doi.org/10.24963/ijcai.2024/964

Sequential decision-making under uncertainty in multi-agent environments is a fundamental problem in artificial intelligence. Games serve as a base model for these problems. Finding optimal plans in games that model real-world scenarios necessitates scalable algorithms. In games with perfect information, algorithms that use a combination of search and deep reinforcement learning can scale to arbitrary-sized games and achieve superhuman performance. In games with imperfect information, the situation is more challenging due to the nature of the search. This work aims to develop algorithms that use search but can scale into larger games than currently possible.
Keywords:
DC: Agent-based and Multi-agent Systems
DC: Game Theory and Economic Paradigms
DC: Search
DC: Machine Learning