人間の指導を活用した優先経験リプレイ強化学習による自動運転
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
人間の指導を組み込んだ強化学習フレームワークを構築し、人間の行動を模倣するオンライン学習モデルと優先経験リプレイを組み合わせて自動運転タスクの学習効率と性能を向上させた論文。
著者: Jingda Wu, Zhiyu Huang, Wenhui Huang, Chen Lv
分類: cs.LG, cs.RO
原文アブストラクト
Reinforcement learning (RL) requires skillful definition and remarkable computational efforts to solve optimization and control problems, which could impair its prospect. Introducing human guidance into reinforcement learning is a promising way to improve learning performance. In this paper, a comprehensive human guidance-based reinforcement learning framework is established. A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm. To relieve the heavy workload on human participants, a behavior model is established based on an incremental online learning method to mimic human actions. We design two challenging autonomous driving tasks for evaluating the proposed algorithm. Experiments are conducted to access the training and testing performance and learning mechanism of the proposed algorithm. Comparative results against the state-of-the-art methods suggest the advantages of our algorithm in terms of learning efficiency, performance, and robustness.
