Autonomous Control of Octopus-Like Ma mpulator Using Reinforcement Learning

摘要

In this paper, we apply reinforcement learning to an octopus-like manipulator. We employ grasping and calling tasks. We show that by designing the manipulator to utilize properties of the real world, the state-action space can he abstracted, and the real-time learning and lack of generalization ability problems can he solved.