提出自适应意图建模框架，让智能体更精准理解对手策略。
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
- 根据任务动态选择多种意图表征的混合方式
- 新表征与自身未来收益互信息最大，更贴近实际效果
- 在多类任务中超越现有方法，适合竞争性多智能体场景
在非合作、竞争性和一般和的多智能体强化学习中，建模对手意图对有效决策至关重要。现有方法使用预设的轨迹信息（如对手下一步动作或未来环境状态）生成意图嵌入来引导主体行为，但这些信息并非普遍适用于所有任务。我们实证发现意图具有任务和环境依赖性。为此，提出一种任务自适应的对手建模框架，学习多个意图表征的性能驱动混合。进一步引入一种最大化与主体未来回报互信息的新意图表征，以捕捉最直接影响表现的对手信息。该方法在多种任务中持续达到或超过当前最优基线性能，并揭示不同建模策略成功的情境与原因。
原文摘要 · Abstract (English)
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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