Article ; Online: Application of Reinforcement Learning in Multiagent Intelligent Decision-Making.
Computational intelligence and neuroscience
2022 Volume 2022, Page(s) 8683616
Abstract: The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a ... ...
| Abstract | The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment. |
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| MeSH term(s) | Algorithms ; Humans ; Intelligence ; Neural Networks, Computer ; Non-alcoholic Fatty Liver Disease ; Reinforcement, Psychology |
| Language | English |
| Publishing date | 2022-09-16 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2388208-6 |
| ISSN | 1687-5273 ; 1687-5273 |
| ISSN (online) | 1687-5273 |
| ISSN | 1687-5273 |
| DOI | 10.1155/2022/8683616 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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