後継特徴量を用いた深層強化学習による類似環境間のナビゲーション
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
迷路状環境でのロボットナビゲーションを、後継特徴量に基づく深層強化学習で解き、学習済みタスクの知識を新環境や新目標へ転移して学習時間を大幅に短縮する手法を提案した。
著者: Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.
