Generating individual intrinsic reward for cooperative multiagent reinforcement learning

Multiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic re...

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Main Authors: Haolin Wu, Hui Li, Jianwei Zhang, Zhuang Wang, Jianeng Zhang
Format: Article
Language:English
Published: SAGE Publishing 2021-10-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/17298814211044946
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author Haolin Wu
Hui Li
Jianwei Zhang
Zhuang Wang
Jianeng Zhang
author_facet Haolin Wu
Hui Li
Jianwei Zhang
Zhuang Wang
Jianeng Zhang
author_sort Haolin Wu
collection DOAJ
description Multiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic reward algorithm, which introduces an intrinsic reward encoder to generate an individual intrinsic reward for each agent and utilizes the hypernetworks as the decoder to help to estimate the individual action values of the decomposition methods based on the generated individual intrinsic reward. Experimental results in the StarCraft II micromanagement benchmark prove that the proposed algorithm can increase learning efficiency and improve policy performance.
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spelling doaj.art-77deac9f7fcb4feb8bc5992f0579ef832022-12-21T23:14:05ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142021-10-011810.1177/17298814211044946Generating individual intrinsic reward for cooperative multiagent reinforcement learningHaolin Wu0Hui Li1Jianwei Zhang2Zhuang Wang3Jianeng Zhang4 College of Computer Science, Sichuan University, Chengdu, China National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China College of Computer Science, Sichuan University, Chengdu, China College of Computer Science, Sichuan University, Chengdu, ChinaMultiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic reward algorithm, which introduces an intrinsic reward encoder to generate an individual intrinsic reward for each agent and utilizes the hypernetworks as the decoder to help to estimate the individual action values of the decomposition methods based on the generated individual intrinsic reward. Experimental results in the StarCraft II micromanagement benchmark prove that the proposed algorithm can increase learning efficiency and improve policy performance.https://doi.org/10.1177/17298814211044946
spellingShingle Haolin Wu
Hui Li
Jianwei Zhang
Zhuang Wang
Jianeng Zhang
Generating individual intrinsic reward for cooperative multiagent reinforcement learning
International Journal of Advanced Robotic Systems
title Generating individual intrinsic reward for cooperative multiagent reinforcement learning
title_full Generating individual intrinsic reward for cooperative multiagent reinforcement learning
title_fullStr Generating individual intrinsic reward for cooperative multiagent reinforcement learning
title_full_unstemmed Generating individual intrinsic reward for cooperative multiagent reinforcement learning
title_short Generating individual intrinsic reward for cooperative multiagent reinforcement learning
title_sort generating individual intrinsic reward for cooperative multiagent reinforcement learning
url https://doi.org/10.1177/17298814211044946
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AT huili generatingindividualintrinsicrewardforcooperativemultiagentreinforcementlearning
AT jianweizhang generatingindividualintrinsicrewardforcooperativemultiagentreinforcementlearning
AT zhuangwang generatingindividualintrinsicrewardforcooperativemultiagentreinforcementlearning
AT jianengzhang generatingindividualintrinsicrewardforcooperativemultiagentreinforcementlearning