HERONの訓練法：2Dレーザーによる湖岸・河岸航行のゼロショットRL
How To Train Your HERON
2Dレーザースキャナのみを用い、シミュレーションでモデルベース強化学習とドメインランダム化により湖岸・河岸追従を学習し、実機の無人水上艇でゼロショット適用に成功した。
著者: Antoine Richard, Stephanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cedric Pradalier
分類: cs.RO, cs.LG
原文アブストラクト
In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner. We train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even though the agent has not been trained in the real world, it can fulfill its task successfully and adapt to changes in the robot's environment and dynamics. Finally, we show that the RL agent is more robust, faster, and more accurate than a state-aware Model-Predictive-Controller.
