BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning

TL;DR

BAMAX is a reinforcement learning-based method that improves multi-agent exploration efficiency by using backtrack assistance, leading to faster environment coverage and reduced backtracking in various grid sizes.

Contribution

This paper introduces BAMAX, a novel backtrack-assisted reinforcement learning approach for multi-agent exploration, addressing coordination challenges in unknown environments.

Findings

BAMAX outperforms traditional methods in exploration speed.

BAMAX reduces the amount of backtracking during exploration.

Performance gains are consistent across different grid sizes.

Abstract

Autonomous robots collaboratively exploring an unknown environment is still an open problem. The problem has its roots in coordination among non-stationary agents, each with only a partial view of information. The problem is compounded when the multiple robots must completely explore the environment. In this paper, we introduce Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning (BAMAX), a method for collaborative exploration in multi-agent systems which attempts to explore an entire virtual environment. As in the name, BAMAX leverages backtrack assistance to enhance the performance of agents in exploration tasks. To evaluate BAMAX against traditional approaches, we present the results of experiments conducted across multiple hexagonal shaped grids sizes, ranging from 10x10 to 60x60. The results demonstrate that BAMAX outperforms other methods in terms of faster coverage and less backtracking across these environments.