Generalized Intention Modeling in Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2605.31318v1
• Date: Fri, 29 May 2026 13:48:18 GMT
• Title: Generalized Intention Modeling in Multi-Agent Reinforcement Learning
• Abstract summary: Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori.<n>We show empirically that this is not the case as intentions are often task- and environment-dependent.<n>We introduce a task-adaptive opponent modeling framework that learns a performance-driven mixture of intent representations.
• Score: 9.77643765859318
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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