Assisted-Value Factorization with Latent Interaction in Cooperate Multi-Agent Reinforcement Learning Article Swipe
Zhitong Zhao
,
Zhang Ya
,
Siying Wang
,
Yang Zhou
,
Ruoning Zhang
,
Wenyu Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13091429
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13091429
With the development of value decomposition methods, multi-agent reinforcement learning (MARL) has made significant progress in balancing autonomous decision making with collective cooperation. However, the collaborative dynamics among agents are continuously changing. The current value decomposition methods struggle to adeptly handle these dynamic changes, thereby impairing the effectiveness of cooperative policies. In this paper, we introduce the concept of latent interaction, upon which an innovative method for generating weights is developed. The proposed method derives weights from the history information, thereby enhancing the accuracy of value estimations. Building upon this, we further propose a dynamic masking mechanism that recalibrates history information in response to the activity level of agents, improving the precision of latent interaction assessments. Experimental results demonstrate the improved training speed and superior performance of the proposed method in both a multi-agent particle environment and the StarCraft Multi-Agent Challenge.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math13091429
- https://www.mdpi.com/2227-7390/13/9/1429/pdf?version=1745725503
- OA Status
- gold
- Cited By
- 1
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409896622
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Raw OpenAlex JSON
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https://openalex.org/W4409896622Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math13091429Digital Object Identifier
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Assisted-Value Factorization with Latent Interaction in Cooperate Multi-Agent Reinforcement LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-27Full publication date if available
- Authors
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Zhitong Zhao, Zhang Ya, Siying Wang, Yang Zhou, Ruoning Zhang, Wenyu ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/math13091429Publisher landing page
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https://www.mdpi.com/2227-7390/13/9/1429/pdf?version=1745725503Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/9/1429/pdf?version=1745725503Direct OA link when available
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Reinforcement learning, Reinforcement, Value (mathematics), Computer science, Factorization, Artificial intelligence, Cognitive psychology, Psychology, Machine learning, Social psychology, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3102905931, https://openalex.org/W3027121709, https://openalex.org/W2012812921, https://openalex.org/W2982599276, https://openalex.org/W6758846586, https://openalex.org/W2166533447, https://openalex.org/W2292533394, https://openalex.org/W2159866602, https://openalex.org/W6630897036, https://openalex.org/W2121092017, https://openalex.org/W4286906273, https://openalex.org/W6634952046, https://openalex.org/W2602275733, https://openalex.org/W2395575420, https://openalex.org/W6760290758, https://openalex.org/W2964937097, https://openalex.org/W2807741983, https://openalex.org/W2794643322, https://openalex.org/W2946606218, https://openalex.org/W3094456479, https://openalex.org/W6781750019, https://openalex.org/W2623431351, https://openalex.org/W2894976951, https://openalex.org/W2617547828, https://openalex.org/W3094349299, https://openalex.org/W6795908048, https://openalex.org/W2747213132, https://openalex.org/W2145339207, https://openalex.org/W2746553466, https://openalex.org/W2173564293, https://openalex.org/W3133438440, https://openalex.org/W1518858799, https://openalex.org/W1579184372, https://openalex.org/W3163926178 |
| referenced_works_count | 34 |
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