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|a 10.1109/TPAMI.2021.3102140
|2 doi
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|a eng
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| 100 |
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|a Li, Wenhao
|e verfasserin
|4 aut
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| 245 |
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|a Structured Cooperative Reinforcement Learning With Time-Varying Composite Action Space
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 05.10.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In recent years, reinforcement learning has achieved excellent results in low-dimensional static action spaces such as games and simple robotics. However, the action space is usually composite, composed of multiple sub-action with different functions, and time-varying for practical tasks. The existing sub-actions might be temporarily invalid due to the external environment, while unseen sub-actions can be added to the current system. To solve the robustness and transferability problems in time-varying composite action spaces, we propose a structured cooperative reinforcement learning algorithm based on the centralized critic and decentralized actor framework, called SCORE. We model the single-agent problem with composite action space as a fully cooperative partially observable stochastic game and further employ a graph attention network to capture the dependencies between heterogeneous sub-actions. To promote tighter cooperation between the decomposed heterogeneous agents, SCORE introduces a hierarchical variational autoencoder, which maps the heterogeneous sub-action space into a common latent action space. We also incorporate an implicit credit assignment structure into the SCORE to overcome the multi-agent credit assignment problem in the fully cooperative partially observable stochastic game. Performance experiments on the proof-of-concept task and precision agriculture task show that SCORE has significant advantages in robustness and transferability for time-varying composite action space
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|a Journal Article
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|a Wang, Xiangfeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jin, Bo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Dijun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zha, Hongyuan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 11 vom: 04. Nov., Seite 8618-8634
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:44
|g year:2022
|g number:11
|g day:04
|g month:11
|g pages:8618-8634
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|u http://dx.doi.org/10.1109/TPAMI.2021.3102140
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