Generalized Intention Modeling in Multi-Agent Reinforcement Learning

2026-05-29

AI Summary

Existing approaches to opponent intention modeling rely on a single predefined information source, limiting their adaptability to varying tasks and environments. This work proposes a task-adaptive hybrid intention modeling framework that dynamically integrates multiple intention representations in a performance-driven manner. Central to the approach is a novel intention embedding mechanism that maximizes mutual information between the embedded intentions and the agent’s future returns. By combining multi-agent reinforcement learning with adaptive representation learning, the method achieves performance on par with or superior to current state-of-the-art baselines across diverse tasks. Furthermore, the study elucidates the conditions under which different opponent modeling strategies are effective and delineates their respective applicability regimes.

Abstract

Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent's next action or a future environment state, and use this to guide the ego-agent's behavior. These approaches assume that the chosen information is universally representative of intent; however, we show empirically that this is not the case as intentions are often task- and environment-dependent. To address this, we introduce a task-adaptive opponent modeling framework that learns a performance-driven mixture of multiple intent representations. We further introduce a new intention representation that maximizes mutual information with the ego-agent's future returns, thereby capturing opponent information that is most directly relevant to performance. Our approach consistently matches or exceeds the performance of state-of-the-art baselines across diverse tasks and yields insights into when and why different opponent modeling strategies succeed.

Problem

• opponent modeling
• intent representation
• multi-agent reinforcement learning
• task-dependent intention
• general-sum games

Innovation

• task-adaptive opponent modeling
• intention representation
• mutual information
• multi-agent reinforcement learning
• performance-driven mixture