序列多智能体强化学习算法

doi:10.16451/j.cnki.issn1003-6059.202103002

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Abstract The multi-agent reinforcement learning algorithm is difficult to adapt to dynamically changing environments of agent scale. Aiming at this problem, a sequence to sequence multi-agent reinforcement learning algorithm(SMARL) based on sequential learning and block structure is proposed. The control network of an agent is divided into action network and target network based on deep deterministic policy gradient structure and sequence-to-sequence structure, respectively, and the correlation between algorithm structure and agent scale is removed. Inputs and outputs of the algorithm are also processed to break the correlation between algorithm policy and agent scale. Agents in SMARL can quickly adapt to the new environment, take different roles in task and achieve fast learning. Experiments show that the adaptability, performance and training efficiency of the proposed algorithm are superior to baseline algorithms.

Key words： Multi-agent Reinforcement Learning Deep Deterministic Policy Gradient(DDPG) Sequence to Sequence(Seq2Seq) Block Structure

Received: 10 October 2020

ZTFLH:

TP 18

Fund:National Natural Science Foundation of China(No.61872260)

Corresponding Authors: WANG Li, Ph.D., professor. Her research interests include artificial intelligence and machine learning.

About author:: SHI Tengfei, master student. His research interests include reinforcement learning.HUANG Zirong, master student. Her research interests include reinforcement lear-ning.

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	SHI Tengfei
	WANG Li
	HUANG Zirong

Cite this article:

SHI Tengfei,WANG Li,HUANG Zirong. Sequence to Sequence Multi-agent Reinforcement Learning Algorithm[J]. , 2021, 34(3): 206-213.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202103002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I3/206

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