Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
arXiv:2607.01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introduce methods of creating datasets of policies for a given game. Second, we propose methods to learn policy representations. Third, we introduce downstream tasks to evaluate the effectiveness of such representations. We evaluate each dataset method, embedding method, and downstream task on Kuhn and Leduc Poker. Although our methods are very basic, we demonstrate that usefu
This research is emerging as AI agents and game theory applications become increasingly sophisticated, pushing the boundaries of autonomous decision-making in complex environments.
Sophisticated policy representations will enable machines to understand and predict opponent behavior more effectively in adversarial scenarios, enhancing AI capabilities in strategy and negotiation.
The ability to learn and embed policies better allows for more robust and adaptive AI agents, particularly in high-stakes, imperfect-information settings.
- · AI/ML researchers
- · Defence tech sector
- · Gaming industry
- · Strategic planning software developers
- · Simpler rule-based AI systems
- · Traditional game AI approaches
Improved AI performance in competitive environments and strategic games due to better policy understanding.
Development of more resilient and unpredictable AI agents that can adapt to obscure opponent strategies.
Potential for breakthroughs in automated negotiation, cyber-warfare planning, and complex supply chain optimization, where imperfect information is common.
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