Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Computer Sciences
Robot Arm Control Method Based On Deep Reinforcement Learning, Heyu Li, Zhilong Zhao, Gu Lei, Liqin Guo, Zeng Bi, Tingyu Lin
Robot Arm Control Method Based On Deep Reinforcement Learning, Heyu Li, Zhilong Zhao, Gu Lei, Liqin Guo, Zeng Bi, Tingyu Lin
Journal of System Simulation
Abstract: Deep reinforcement learning continues to explore in the environment and adjusts the neural network parameters by the reward function. The actual production line can not be used as the trial and error environment for the algorithm, so there is not enough data. For that, this paper constructs a virtual robot arm simulation environment, including the robot arm and the object. The Deep Deterministic Policy Gradient (DDPG),in which the state variables and reward function are set,is trained by deep reinforcement learning algorithm in the simulation environment to realize the target of controlling the robot arm to move the gripper below …
Dp-Q(Λ): Real-Time Path Planning For Multi-Agent In Large-Scale Web3d Scene, Fengting Yan, Jinyuan Jia
Dp-Q(Λ): Real-Time Path Planning For Multi-Agent In Large-Scale Web3d Scene, Fengting Yan, Jinyuan Jia
Journal of System Simulation
Abstract: The path planning of multi-agent in an unknown large-scale scene needs an efficient and stable algorithm, and needs to solve multi-agent collision avoidance problem, and then completes a real-time path planning in Web3D. To solve above problems, the DP-Q(λ) algorithm is proposed; and the direction constraints, high reward or punishment weight training methods are used to adjust the values of reward or punishment by using a probability p (0-1 random number). The value from reward or punishment determines its next step path planning strategy. If the next position is free, the agent could walk to it. The above strategy …