Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs

Zhen Zhang; Dongbin Zhao

Volume 1 Issue 3

Jul. 2014

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2014 > 1(3): 248-256

Zhen Zhang and Dongbin Zhao, "Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 248-256, 2014.

Citation:

Zhen Zhang and Dongbin Zhao, "Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 248-256, 2014.

Zhen Zhang and Dongbin Zhao, "Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 248-256, 2014.

Citation:

Zhen Zhang and Dongbin Zhao, "Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 248-256, 2014.

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Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs

Zhen Zhang¹,
Dongbin Zhao²

1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China and Department of Electric Engineering, College of Automation Engineering, Qingdao University, Qingdao 266071, China;
2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Funds:

This work was supported by National Natural Science Foundation of China (61273136, 61034002), Beijing Natural Science Foundation (4122083), and Visiting Professorship of Chinese Academy of Sciences.

Abstract

Abstract

In this paper, we propose a clique-based sparse reinforcement learning (RL) algorithm for solving cooperative tasks. The aim is to accelerate the learning speed of the original sparse RL algorithm and to make it applicable for tasks decomposed in a more general manner. First, a transition function is estimated and used to update the Q-value function, which greatly reduces the learning time. Second, it is more reasonable to divide agents into cliques, each of which is only responsible for a specific subtask. In this way, the global Q-value function is decomposed into the sum of several simpler local Q-value functions. Such decomposition is expressed by a factor graph and exploited by the general maxplus algorithm to obtain the greedy joint action. Experimental results show that the proposed approach outperforms others with better performance.
- Terms—Multiagent reinforcement learning,
- factor graph,
- max-plus algorithm,
- clique-based decomposition

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References(50)

References

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