Stage-Wise Learning of Reaching Using Little Prior Knowledge.

ABSTRACT: In some manipulation robotics environments, because of the difficulty of precisely modeling dynamics and computing features which describe well the variety of scene appearances, hand-programming a robot behavior is often intractable. Deep reinforcement learning methods partially alleviate this problem in that they can dispense with hand-crafted features for the state representation and do not need pre-computed dynamics. However, they often use prior information in the task definition in the form of shaping rewards which guide the robot toward goal state areas but require engineering or human supervision and can lead to sub-optimal behavior. In this work we consider a complex robot reaching task with a large range of initial object positions and initial arm positions and propose a new learn

SUBMITTER: de La Bourdonnaye F 

PROVIDER: S-EPMC7806066 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature