モデルベース後知恵経験再生
MHER: Model-based Hindsight Experience Replay
学習したダイナミクスモデルで仮想的な達成ゴールを生成し、それを用いて経験を再ラベリングすることで、多ゴール強化学習のサンプル効率を大幅に改善する手法を提案した論文。
著者: Rui Yang, Meng Fang, Lei Han, Yali Du, Feng Luo, Xiu Li
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewards. However, these methods are still limited in efficiency and cannot make full use of experiences. In this paper, we propose Model-based Hindsight Experience Replay (MHER), which exploits experiences more efficiently by leveraging environmental dynamics to generate virtual achieved goals. Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR). Based on MBR, MHER performs both reinforcement learning and supervised learning for efficient policy improvement. Theoretically, we also prove the supervised part in MHER, i.e., goal-conditioned supervised learning with MBR data, optimizes a lower bound on the multi-goal RL objective. Experimental results in several point-based tasks and simulated robotics environments show that MHER achieves significantly higher sample efficiency than previous model-free and model-based multi-goal methods.
