A deep reinforcement learning-based method applied for solving multi-agent defense and attack problems

作者：

Highlights：

• The multi-agent defense and attack environment is reconstructed.

• Several algorithms are applied to solve the considered problem.

• We redefine the state space, the action space and the reward functions accordingly.

• Comparison experiments are conducted to show the performance of the employed models.

摘要

•The multi-agent defense and attack environment is reconstructed.•Several algorithms are applied to solve the considered problem.•We redefine the state space, the action space and the reward functions accordingly.•Comparison experiments are conducted to show the performance of the employed models.

论文关键词：Multi-agent cooperation,Defense and attack,Deep reinforcement learning,Multi-agent reinforcement learning

论文评审过程：Received 28 June 2020, Revised 23 October 2020, Accepted 7 March 2021, Available online 17 March 2021, Version of Record 3 April 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114896