让智能体主动展示意图，提升协作效率。
Active Legibility in Multiagent Reinforcement Learning
- 设计可主动展现意图的行动机制，增强同伴理解
- 实验表明训练耗时更少，性能优于主流算法
- 适合需要高效协同的多智能体场景
多智能体序列决策问题广泛应用于城市交通、自动驾驶、军事行动等关键领域。近年来，多智能体强化学习发展迅速，其中建模其他智能体行为的范式引起了关注，区别于传统的值分解或通信机制。该范式使智能体能够理解并预测他人行为，促进协作。受近期关于“可读性”研究启发——即智能体通过行为展示意图——我们提出一种多智能体主动可读性框架，使智能体采取可读动作以帮助他人优化行为。同时，我们构建了一系列模拟常见场景的问题域，以充分刻画多智能体强化学习中的可读性。实验结果表明，新框架在性能上更优，且训练时间显著减少，相比多种主流多智能体强化学习算法更具效率。
原文摘要 · Abstract (English)
A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework allows agents to conduct legible actions so as to help others optimise their behaviors. In addition, we design a series of problem domains that emulate a common scenario and best characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and costs less training time compared to several multiagent reinforcement learning algorithms.
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