Research on Multi-Robot Formation Control Based on MATD3 Algorithm

This paper investigates the problem of multi-robot formation control strategies in environments with obstacles based on deep reinforcement learning methods. To solve the problem of value function overestimation in the deep deterministic policy gradient (DDPG) algorithm, this paper proposes an improv...

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Vydáno v:Applied sciences Ročník 13; číslo 3; s. 1874
Hlavní autoři: Zhou, Conghang, Li, Jianxing, Shi, Yujing, Lin, Zhirui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2023
Témata:
Algorithms
formation control
MATD3 algorithm
multi-robot
reinforcement learning
Robots
Velocity
ISSN:2076-3417, 2076-3417
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Shrnutí:This paper investigates the problem of multi-robot formation control strategies in environments with obstacles based on deep reinforcement learning methods. To solve the problem of value function overestimation in the deep deterministic policy gradient (DDPG) algorithm, this paper proposes an improved multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm under the CTDE framework combined with the twin delayed deep deterministic policy gradient (TD3) algorithm, which adopts a prioritized experience replay strategy to improve the learning efficiency. For the problem of difficult obstacle avoidance for a robot formation, a hybrid reward mechanism is designed to use different formation maintenance strategies in obstacle areas and obstacle-free areas to achieve the control goal of obstacle avoidance by reasonably changing the formation. The simulation experiments verified the effectiveness of the multi-robot formation control strategy designed in this paper, and comparative simulations verified that the algorithm has a faster convergence speed and more stable performance.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13031874