CASIA OpenIR  > 复杂系统认知与决策实验室  > 飞行器智能技术
Improving Generalization of Multi-agent Reinforcement Learning through Domain-Invariant Feature Extraction
Xu YF(徐一凡); Pu ZQ(蒲志强); Cai QA(蔡奇昂); Li FM(李非墨); Chai XH(柴兴华)
2023-09
Conference NameInternational Conference on Artificial Neural Networks
Conference Date2023-5
Conference PlaceGreece
Abstract

The limited generalization ability of reinforcement learning
constrains its potential applications, particularly in complex scenarios
such as multi-agent systems. To overcome this limitation and enhance
the generalization capability of MARL algorithms, this paper proposes
a three-stage method that integrates domain randomization and domain
adaptation to extract effective features for policy learning. Specifically,
the first stage samples environments provided for training and testing
in the following stages using domain randomization. The second stage
pretrains a domain-invariant feature extractor (DIFE) which employs
cycle consistency to disentangle domain-invariant and domain-specific
features. The third stage utilizes DIFE for policy learning. Experimental
results in MPE tasks demonstrate that our approach yields better performance
and generalization ability. Meanwhile, the features captured by
DIFE are more interpretable for subsequent policy learning in visualization
analysis.

Indexed ByEI
Sub direction classification多智能体系统
planning direction of the national heavy laboratory多智能体决策
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57458
Collection复杂系统认知与决策实验室_飞行器智能技术
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xu YF,Pu ZQ,Cai QA,et al. Improving Generalization of Multi-agent Reinforcement Learning through Domain-Invariant Feature Extraction[C],2023.
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