Structure of RGRL. (IMAGE)
Caption
Deepreinforcement learning has disadvantages such as low sample utilization and slow convergence, and thousandsof trial-and-error iterations are required to perform reinforcement learning in realistic scenarios, which iscostly. To alleviate this problem, RGRL first simulates a robot grasping an object from a user in a simulatedscene in which tens of thousands of learning sessions are performed. Domain randomization is used to narrowthe gap between the simulated and real scenes, and a multi-objective reward function is used to effectivelyaccelerate the convergence of the reinforcementlearning algorithm.
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Beijing Zhongke Journal Publising Co. Ltd.
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