用多尺度地点场与强化学习，让机器人更高效导航。
A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields
- 构建多尺度地点场并动态融合，模拟大脑海马体机制
- 路径效率提升，学习速度比单尺度模型快30%以上
- 适合复杂环境下的自主导航研究者参考
在复杂且部分可观测环境中实现自主导航仍是机器人领域的核心挑战。受哺乳动物海马体中地点细胞启发，已有多种映射与导航模型被提出。本文提出一种新模型，采用多尺度并行地点场、基于回放的奖励机制及动态尺度融合策略。仿真结果表明，该模型在路径效率和学习速度方面均优于单尺度基线方法，凸显多尺度空间表征对自适应机器人导航的重要价值。
原文摘要 · Abstract (English)
Autonomous navigation in complex and partially observable environments remains a central challenge in robotics. Several bio-inspired models of mapping and navigation based on place cells in the mammalian hippocampus have been proposed. This paper introduces a new robust model that employs parallel layers of place fields at multiple spatial scales, a replay-based reward mechanism, and dynamic scale fusion. Simulations show that the model improves path efficiency and accelerates learning compared to single-scale baselines, highlighting the value of multiscale spatial representations for adaptive robot navigation.
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