多様な動的エージェント間での深層強化学習による衝突回避計画
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
他エージェントの行動を仮定せず、LSTMで任意数の周囲エージェントを扱える深層強化学習ベースの衝突回避アルゴリズムを開発し、実機ロボットで実証した。
著者: Michael Everett, Yu Fan Chen, Jonathan P. How
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.
