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arXiv · 2303.14061

Learning Reward Machines in Cooperative Multi-Agent Tasks

Leo Ardon·Daniel Furelos-Blanco·Alessandra Russo
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Abstract

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.

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Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo. 2023-05-24. Learning Reward Machines in Cooperative Multi-Agent Tasks. https://doi.org/10.1007/978-3-031-56255-6_3

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