Distributed Computing and Artificial Intelligence pp 553-556 | Cite as
Autonomous Control of Octopus-Like Manipulator Using Reinforcement Learning
Conference paper
Abstract
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 be abstracted, and the real-time learning and lack of generalization ability problems can be solved.
Keywords
Generalization Abstraction of state-action GraspingPreview
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