Robotic Arm Control and Task Training through Deep Reinforcement Learning

6 May 2020Andrea FranceschettiElisa ToselloNicola CastamanStefano Ghidoni

This paper proposes a detailed and extensive comparison of the Trust Region Policy Optimization and DeepQ-Network with Normalized Advantage Functions with respect to other state of the art algorithms, namely Deep Deterministic Policy Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former have better performances then the latter when asking robotic arms to accomplish manipulation tasks such as reaching a random target pose and pick &placing an object... (read more)

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