Learning Reward Machines in Cooperative Multi-Agent Tasks

• URL: http://arxiv.org/abs/2303.14061v4
• Date: Wed, 24 May 2023 07:20:20 GMT
• Title: Learning Reward Machines in Cooperative Multi-Agent Tasks
• Authors: Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo
• Abstract summary: This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) It 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.
• Score: 75.79805204646428
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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