Competitive deep reinforcement learning for robot control

Lin Jiang; Weiyue Zhang; Jianyi Zhu

doi:10.1117/12.2675162

25 May 2023 Competitive deep reinforcement learning for robot control

Lin Jiang, Weiyue Zhang, Jianyi Zhu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360K (2023) https://doi.org/10.1117/12.2675162
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

This paper presents a competitive deep reinforcement learning (DRL) structure for the robot control, the basic method of which is to make the two actors learn from each other with competition. And the improvement of training efficiency and learning ability of the competitive DRL model is verified by the experiments under the Gym environments. For the task of robot formation, we construct a cooperation mechanism exerting robot to take the optimal action due to the state and the next reward of other robots within a certain radius around based on the Kuhn-Munkres algorithm, to avoid collisions and blocking among robots. Lastly, we conduct the simulations with the above algorithms, and the results illustrate that the trained robots are capable of self-navigation, obstacle avoidance and formation without collisions.

Citation Download Citation

Lin Jiang, Weiyue Zhang, and Jianyi Zhu "Competitive deep reinforcement learning for robot control", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360K (25 May 2023); https://doi.org/10.1117/12.2675162

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