深層強化学習による能動的ロボットマッピング
Active Robotic Mapping through Deep Reinforcement Learning
環境をできるだけ早く地図化するための探索方策を深層強化学習で学習し、災害マッピングのシミュレーションで近最適な近視的探索をわずかに上回る性能を示した。
著者: Shane Barratt
分類: cs.RO
原文アブストラクト
We propose an approach to learning agents for active robotic mapping, where the goal is to map the environment as quickly as possible. The agent learns to map efficiently in simulated environments by receiving rewards corresponding to how fast it constructs an accurate map. In contrast to prior work, this approach learns an exploration policy based on a user-specified prior over environment configurations and sensor model, allowing it to specialize to the specifications. We evaluate the approach through a simulated Disaster Mapping scenario and find that it achieves performance slightly better than a near-optimal myopic exploration scheme, suggesting that it could be useful in more complicated problem scenarios.