多智能体协作探索新环境，用回溯辅助提升效率。
BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning
- 引入回溯辅助机制，优化多智能体探索路径
- 在10x10至60x60网格中覆盖更快、回溯更少
- 适合需要高效全覆盖的机器人协同任务
自主机器人在未知环境中协同探索仍是开放问题，根源在于非平稳智能体间的信息不完整与协调困难。当多个机器人需完全探索环境时，挑战更为严峻。本文提出基于强化学习的回溯辅助多智能体探索方法（BAMAX），旨在实现对整个虚拟环境的高效探索。实验在多种六边形网格环境下进行，尺寸从10x10到60x60不等。结果表明，BAMAX在覆盖速度和回溯次数上均优于传统方法。
原文摘要 · Abstract (English)
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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