论文标题
电力供应商的战略招标的强化学习方法，信息不足
A Reinforcement Learning Method For Power Suppliers' Strategic Bidding with Insufficient Information
论文作者
论文摘要
电力供应商可以行使市场能力以获取更高的利润。但是，当外部信息极为罕见时，这将变得困难。为了在非常不完整的信息市场环境中获得有希望的表现，本文提出了一种基于学习自动机（LA）的新型无模型的增强学习算法。此外，本文分析了基于Cournot市场模型的案例研究中算法的合理性和融合。
Power suppliers can exercise market power to gain higher profit. However, this becomes difficult when external information is extremely rare. To get a promising performance in an extremely incomplete information market environment, a novel model-free reinforcement learning algorithm based on the Learning Automata (LA) is proposed in this paper. Besides, this paper analyses the rationality and convergence of the algorithm in case studies based on the Cournot market model.
