Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives
Dongwon Son,Myungsin Kim,Jaecheol Sim,Wonsik Shin,Dongwon Son,Myungsin Kim,Jaecheol Sim,Wonsik Shin
The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primitives), that can efficiently solve the object manipulation with visual input and sparse reward, by uti...
