Abstract
In this research, we present a novel solution to the problem of multi-robot task allocation (MRTA) by utilizing a variant of the state-of-art Deep Reinforcement Learning algorithm, Soft Actor-Critic (SAC) within Multirobot Navigation Systems(MRS). Our approach addresses the existing research gaps in MRTA for MRS, which have received limited attention and have proven challenging to solve using conventional methods. Through extensive simulation evaluations, we demonstrate that our system outperforms current state-of-the-art methods in terms of performance metrics. These findings highlight the effectiveness and superiority of our proposed approach in overcoming the inherent challenges of large-scale MRS. Additionally, we contribute to the field by proposing improvements to the Task Allocation Index (TAI) metric, enabling a more comprehensive evaluation of MRTA performance.
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