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<p>Despite recent advances in multi-agent path finding, achieving robust coordination in dynamic and crowded warehouse environments remains a bottleneck due to training instability and inefficient credit assignment. To address these challenges, we propose BeMapper, a novel evolutionary-augmented reinforcement learning framework that integrates a multi-agent bidirectionally-coordinated network (BicNet) with a distributed actor-critic architecture. Technically, our core novelty lies in three aspects: (1) A bidirectional feature fusion mechanism that enables agents to perceive collective spatial states beyond local observations; (2) An evolutionary-driven critic selection strategy that iteratively propagates high-performing models to accelerate convergence; (3) A multi-metric scoring system that incorporates success rate variance to penalize unstable behaviors and resolve credit assignment ambiguity. Extensive experiments demonstrate the superiority of BeMapper: it achieves a 98.66% mean success rate, outperforming state-of-the-art baselines Mapper (95.51%) and BicNet (93.78%) by 3.15%and 4.88%, respectively. Crucially, BeMapper yields a significantly higher average reward of 18.81, representing a relative improvement of 1.65 over Mapper and a substantial leap over BicNet’s near-zero performance (0.04). Furthermore, in more crowded scenarios, BeMapper reduces the average travel steps to 36, being 5–9 steps shorter than competing methods, effectively enhancing operational throughput while ensuring robustness for large-scale industrial automation.</p>

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BeMapper: BicNet and evolutionary-based multi-agent path planning with effective reinforcement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月12日

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  • Chenxuan Ma,
  • Yizhuo Lei

摘要

Despite recent advances in multi-agent path finding, achieving robust coordination in dynamic and crowded warehouse environments remains a bottleneck due to training instability and inefficient credit assignment. To address these challenges, we propose BeMapper, a novel evolutionary-augmented reinforcement learning framework that integrates a multi-agent bidirectionally-coordinated network (BicNet) with a distributed actor-critic architecture. Technically, our core novelty lies in three aspects: (1) A bidirectional feature fusion mechanism that enables agents to perceive collective spatial states beyond local observations; (2) An evolutionary-driven critic selection strategy that iteratively propagates high-performing models to accelerate convergence; (3) A multi-metric scoring system that incorporates success rate variance to penalize unstable behaviors and resolve credit assignment ambiguity. Extensive experiments demonstrate the superiority of BeMapper: it achieves a 98.66% mean success rate, outperforming state-of-the-art baselines Mapper (95.51%) and BicNet (93.78%) by 3.15%and 4.88%, respectively. Crucially, BeMapper yields a significantly higher average reward of 18.81, representing a relative improvement of 1.65 over Mapper and a substantial leap over BicNet’s near-zero performance (0.04). Furthermore, in more crowded scenarios, BeMapper reduces the average travel steps to 36, being 5–9 steps shorter than competing methods, effectively enhancing operational throughput while ensuring robustness for large-scale industrial automation.