TY - GEN
T1 - Multiagent Motion Planning Based on Deep Reinforcement Learning in Complex Environments
AU - Wu, Dingwei
AU - Wan, Kaifang
AU - Gao, Xiaoguang
AU - Hu, Zijian
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/16
Y1 - 2021/4/16
N2 - When agents in a multiagent system implement motion planning in complex and dynamic environments, model-based planning algorithms have poor adaptability, while intelligent algorithms, such as MADDPG, encounter difficulty in converging when training multiple agents, and the resulting control model has poor stability and robustness. To address the above challenges, this paper proposes a mixed experience multiagent deep deterministic policy gradient algorithm referred to as ME-MADDPG. The algorithm increases the high-quality experience obtained by artificial potential field method and uses dynamic probability to sample from different replay buffers. Simulation experiments have proven that compared to MADDPG, ME-MADDPG greatly improves convergence speed, convergence effect and stability and that ME-MADDPG can efficiently provide shorter and more convenient paths for multiagent systems.
AB - When agents in a multiagent system implement motion planning in complex and dynamic environments, model-based planning algorithms have poor adaptability, while intelligent algorithms, such as MADDPG, encounter difficulty in converging when training multiple agents, and the resulting control model has poor stability and robustness. To address the above challenges, this paper proposes a mixed experience multiagent deep deterministic policy gradient algorithm referred to as ME-MADDPG. The algorithm increases the high-quality experience obtained by artificial potential field method and uses dynamic probability to sample from different replay buffers. Simulation experiments have proven that compared to MADDPG, ME-MADDPG greatly improves convergence speed, convergence effect and stability and that ME-MADDPG can efficiently provide shorter and more convenient paths for multiagent systems.
KW - deep reinforcement learning
KW - MADDPG
KW - motion planning
KW - multiagent
UR - http://www.scopus.com/inward/record.url?scp=85107775603&partnerID=8YFLogxK
U2 - 10.1109/ICCRE51898.2021.9435656
DO - 10.1109/ICCRE51898.2021.9435656
M3 - 会议稿件
AN - SCOPUS:85107775603
T3 - 2021 6th International Conference on Control and Robotics Engineering, ICCRE 2021
SP - 123
EP - 128
BT - 2021 6th International Conference on Control and Robotics Engineering, ICCRE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Control and Robotics Engineering, ICCRE 2021
Y2 - 16 April 2021 through 18 April 2021
ER -