BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning

2024/11/13 by Gaurav Kalra, Amit Patel, Kalra, Geetansh +5
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2411.08400

openalex publication_date 2024/11/13 · openalex created_date 2024/11/16 · openalex updated_date 2026/07/28

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.

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