バックトラック支援による強化学習を用いたマルチエージェント探索
BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning
強化学習とバックトラック支援を組み合わせ、複数ロボットが未知環境を効率的に協調探索する手法BAMAXを提案し、従来法より速い被覆と少ない後戻りを実現した。
著者: Geetansh Kalra, Amit Patel, Atul Chaudhari, Divye Singh
分類: cs.RO, cs.AI
原文アブストラクト
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.