BAMAX：基于强化学习的回溯辅助多智能体探索
BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning
AI总结：
针对多机器人协作完全探索未知环境的难题，提出基于强化学习的回溯辅助多智能体探索方法BAMAX，利用回溯机制提升探索效率，实验证明其在多种六边形网格中实现了更快的覆盖率和更少的回溯。
摘要：
自主机器人协作探索未知环境仍然是一个开放问题。该问题源于非平稳智能体之间的协调，每个智能体仅拥有信息的局部视图。当多个机器人必须完全探索环境时，该问题变得更加复杂。在本文中，我们提出了基于强化学习的回溯辅助多智能体探索（BAMAX），这是一种用于多智能体系统协作探索的方法，旨在探索整个虚拟环境。顾名思义，BAMAX利用回溯辅助来提升智能体在探索任务中的性能。为了将BAMAX与传统方法进行对比评估，我们展示了在多种六边形网格尺寸（从10x10到60x60）上进行的实验结果。结果表明，在这些环境中，BAMAX在更快的覆盖率和更少的回溯方面优于其他方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
