AB-Mapper: Attention and BicNet Based Multi-agent Path Finding for Dynamic Crowded Environment

TL;DR

AB-Mapper is a novel multi-agent path planning algorithm that leverages attention mechanisms and BicNet within an actor-critic reinforcement learning framework to improve success rates in dynamic crowded environments.

Contribution

The paper introduces AB-Mapper, combining attention and BicNet for enhanced intra-team coordination and environment awareness in multi-agent path finding.

Findings

Achieves 85.86% success rate, outperforming Mapper's 81.56%.

Significantly better performance in crowded scenarios, with over 40% improvement.

Effective in dynamic environments with obstacles and high agent density.

Abstract

Multi-agent path finding in dynamic crowded environments is of great academic and practical value for multi-robot systems in the real world. To improve the effectiveness and efficiency of communication and learning process during path planning in dynamic crowded environments, we introduce an algorithm called Attention and BicNet based Multi-agent path planning with effective reinforcement (AB-Mapper)under the actor-critic reinforcement learning framework. In this framework, on the one hand, we utilize the BicNet with communication function in the actor-network to achieve intra team coordination. On the other hand, we propose a centralized critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by also considering the behaviours of its surrounding agents. Compared with the state-of-the-art method Mapper,our AB-Mapper is more effective (85.86% vs. 81.56% in terms of success rate) in solving the general path finding problems with dynamic obstacles. In addition, in crowded scenarios, our method outperforms the Mapper method by a large margin,reaching a stunning gap of more than 40% for each experiment.