LS-IQ: 逆強化学習のための暗黙的報酬正則化
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
暗黙的報酬の二乗ノルム正則化を理論的に解析し、吸収状態を適切に扱う最小二乗逆Q学習（LS-IQ）を提案。専門家の行動なしでも逆動力学モデルで学習可能。
著者: Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states. Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theoretical analysis of the resulting properties of the algorithms. In this work, we show that using this regularizer under a mixture distribution of the policy and the expert provides a particularly illuminating perspective: the original objective can be understood as squared Bellman error minimization, and the corresponding optimization problem minimizes a bounded $\chi^2$-Divergence between the expert and the mixture distribution. This perspective allows us to address instabilities and properly treat absorbing states. We show that our method, Least Squares Inverse Q-Learning (LS-IQ), outperforms state-of-the-art algorithms, particularly in environments with absorbing states. Finally, we propose to use an inverse dynamics model to learn from observations only. Using this approach, we retain performance in settings where no expert actions are available.
