時系列マニピュレーションタスクにおける深層強化学習の帰納バイアスとしての時間インデックス付け

原文アブストラクト

While solving complex manipulation tasks, manipulation policies often need to learn a set of diverse skills to accomplish these tasks. The set of skills is often quite multimodal - each one may have a quite distinct distribution of actions and states. Standard deep policy-learning algorithms often model policies as deep neural networks with a single output head (deterministic or stochastic). This structure requires the network to learn to switch between modes internally, which can lead to lower sample efficiency and poor performance. In this paper we explore a simple structure which is conducive to skill learning required for so many of the manipulation tasks. Specifically, we propose a policy architecture that sequentially executes different action heads for fixed durations, enabling the learning of primitive skills such as reaching and grasping. Our empirical evaluation on the Metaworld tasks reveals that this simple structure outperforms standard policy learning methods, highlighting its potential for improved skill acquisition.