This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments and improves the interpretability of the learnt policies required to complete the cooperative task. The RMs associated with each sub-task are learnt in a decentralised manner and then used to guide the behaviour of each agent. By doing so, the complexity of a cooperative multi-agent problem is reduced, allowing for more effective learning. The results suggest that our approach is a promising direction for future research in MARL, especially in complex environments with large state spaces and multiple agents.

翻译：本文提出了一种新颖的多智能体强化学习方法，该方法将合作任务分解与学习编码子任务结构的奖励机相结合。所提出的方法有助于处理部分可观测环境中奖励的非马尔可夫性质，并提升了完成合作任务所需学习策略的可解释性。每个子任务关联的奖励机以去中心化方式学习，并用于指导每个智能体的行为。通过这种方式，合作多智能体问题的复杂度得以降低，从而允许更高效的学习。结果表明，我们的方法为多智能体强化学习的未来研究提供了一个有前景的方向，特别是在具有大状态空间和多个智能体的复杂环境中。