March 13, 2024, 4:42 a.m. | Xuejing Zheng, Chao Yu

cs.LG updates on

arXiv:2403.07005v1 Announce Type: cross
Abstract: In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL) problems using Reward Machines (RMs) to specify the reward functions such that the prior knowledge of high-level events in a task can be leveraged to facilitate the learning efficiency. Unlike the existing work that RMs have been incorporated into MARL for task decomposition and policy learning in relatively simple domains or with an assumption of independencies among the agents, we present Multi-Agent Reinforcement Learning …

abstract agent arxiv cs.lg efficiency events functions knowledge machines multi-agent paper prior reinforcement reinforcement learning study type work

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