Generalized Intention Modeling in Multi-Agent Reinforcement Learning
This work is significant for researchers and practitioners in multi-agent reinforcement learning, as it improves opponent modeling by adapting to task- and environment-dependent intentions, leading to better decision-making in competitive scenarios.
This paper tackles the problem of modeling opponent intent in multi-agent reinforcement learning, where existing methods assume a universally representative intent. The authors introduce a task-adaptive framework that learns a mixture of intent representations and a new representation maximizing mutual information with the ego-agent's future returns, consistently matching or exceeding state-of-the-art baselines.
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.