ScholarWorks@숙명여자대학교

초록

This paper proposed a similarity-based multi-knowledge transfer algorithm (SMTRL) to enhance the learning efficiency of autonomous agents in reinforcement learning. SMTRL can calculates the similarity between pre-trained models and the current model and dynamically adjust the knowledge transfer ratio based on this similarity to maximize learning efficiency. In complex environments, autonomous agents face significant challenges when learning independently, as this process can be time-consuming and inefficient, making knowledge transfer essential. However, differences between pre-trained models and actual environments can result in negative transfer, leading to diminished learning performance. To tackle this issue, SMTRL dynamically can adjusts the ratio of knowledge transfer from highly similar pre-trained models, thereby accelerating learning stability. Furthermore, experimental results demonstrated that the proposed algorithm outperformed traditional reinforcement learning and traditional knowledge transfer learning in terms of convergence speed. Therefore, this paper introduces a novel approach to efficient knowledge transfer for autonomous agents and discusses its applicability to complex mobility environments and directions for future research.

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