| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 賴龍斌 | zh_TW |
| DC.creator | Long-Bin Lai | en_US |
| dc.date.accessioned | 2024-7-11T07:39:07Z | |
| dc.date.available | 2024-7-11T07:39:07Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111225023 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 本文探討了能源市場中的拍賣行為，使用多代理模型進行模擬。我們將電力供應商和消費者建模為自主代理，他們在多代理環境中做出決策以最大化其效用。然而，由於代理之間的信息不足，每個代理都難以實現其最佳決策。為了解決這個問題，我們提出使用納許Q學習，它結合了納許均衡和Q學習，以在考慮其他代理出價行為的同時最大化每個參與者的效用。在多個案例研究中，我們證明了納許Q學習算法能夠確保參與者最終達到納許均衡。 | zh_TW |
| dc.description.abstract | This paper explores auction behavior in the energy market using a multi-agent model. We model electricity suppliers and consumers as autonomous agents who make decisions to maximize their utilities in a multi-agent environment. However, due to insufficient information between the agents, each agent faces difficulty achieving his/her optimal decision. To address this issue, we propose using Nash Q-learning, consisting of Nash equilibrium and Q-learning, to maximize each participant′s utility while considering the bidding behavior of the other agents. In several case studies, we demonstrate that the Nash Q-learning algorithm ensures participants eventually reach the Nash equilibriums. | en_US |
| DC.subject | 納許均衡 | zh_TW |
| DC.subject | Q學習 | zh_TW |
| DC.subject | 強化學習 | zh_TW |
| DC.subject | Nash equilibrium | en_US |
| DC.subject | Q-learning | en_US |
| DC.subject | reinforcement learning | en_US |
| DC.title | 利用強化學習探索可再生能源交易市場中的參與者策略 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Exploring Participant Strategies in Renewable Energy Trading Markets Using Reinforcement Learning | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |