Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 247-256.doi: 10.11896/jsjkx.210700100

• Artificial Intelligence • Previous Articles     Next Articles

Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning

SHI Dian-xi1,2,4, ZHAO Chen-ran1, ZHANG Yao-wen3, YANG Shao-wu1, ZHANG Yong-jun2   

  1. 1 School of Computer Science,National University of Defense Technology,Changsha 410073,China
    2 National Innovation Institute of Defense Technology,Academy of Military Sciences,Beijing 100166,China
    3 Unit 32282 of People’s Liberation Army of China,Jinan 250000,China
    4 Tianjin Artificial Intelligence Innovation Center,Tianjin 300457,China
  • Received:2021-07-09 Revised:2022-01-05 Published:2022-08-02
  • About author:SHI Dian-xi,born in 1966,Ph.D,professor,Ph.D supervisor.His main research interests include distributed object middleware technology,adaptive software technology,artificial intelligence and robot operating systems.
    ZHANG Yong-Jun,born in 1966,Ph.D,professor.His main research interests include artificial intelligence,multi-agent cooperation,machine learning and feature recognition.
  • Supported by:
    National Natural Science Foundation of China(91948303).

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Abstract: At present,most multi-agent reinforcement learning(MARL) algorithms using the architecture of centralized training and decentralized execution(CTDE) have good results in homogeneous multi-agent systems.However,for heterogeneous multi-agent systems composed of different roles,there is always the problem of credit assignment,which makes it difficult for agents to learn effective cooperation strategies.To tackle the above problems,an adaptive reward method with end-to-end cooperation based on multi-agent reinforcement learning is proposed.It can promote the cooperation between agents.First,a batch regularization network is proposed.It uses a graph neural network to model the cooperative relationship of heterogeneous multi-agents.And it uses the attention mechanism to calculate the weight of key information.Also,it uses the batch regularization method to generate feature vectors.Besides,it guides the algorithm to learn in the right direction,thereby effectively improving the performance of heterogeneous multi-agent cooperative strategy generation.Second,an adaptive intrinsic reward network based on the actor-critic method is proposed.It can convert sparse rewards into dense rewards,which can guide agents to generate cooperative strategies according to the situation on the field.Through experiments,compared with the current mainstream multi-agent reinforcement learning algorithms,the proposed method has achieved significantly good results in the “cooperative-game” scenario.In addition,the visual analysis of the strategy-reward-behavior correlation further verifies the effectiveness of the proposed method.

Key words: Adaptive intrinsic reward, Graph attention network, Multi-agent reinforcement learning

CLC Number: 

  • TP391
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