一种提升机器人强化学习开发效率的训练模式研究

doi:10.12052/gdutxb.200009

Abstract

Abstract: Deep reinforcement learning (DRL) model combining reinforcement learning and deep learning is currently widely used in the field of robot control. Robot reinforcement learning needs to train the model in a 3D simulation environment. However, in the absence of prior environmental knowledge, trial and error learning in a 3D environment leads to long training cycles and high development costs. To solve this problem, a training mode from 2D to 3D is proposed. Time-consuming and computationally intensive work is completed in a 2D environment, and the results are transferred to a 3D environment for testing. Experiments show that this training mode can improve the development efficiency by about five times, so that personal computers can also do research related to robot reinforcement learning.

Key words: deep reinforcement learning, robot control, training mode, development efficiency

CLC Number:

TP242.6

Ye Wei-jie, Gao Jun-li, Jiang Feng, Guo Jing. A Research on a Training Model to Improve the Development Efficiency of Robot Reinforcement Learning[J].Journal of Guangdong University of Technology, 2020, 37(05): 46-50.

References

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