论文标题
Gencos的行为基于Q学习的模型，改善了二分法
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy
论文作者
论文摘要
Q学习被广泛用于模拟电力市场中的发电公司（Gencos）的行为。但是，现有的Q学习方法通常需要许多迭代来融合，这在实践中耗时且效率低下。为了提高计算效率，本文提出了一种新颖的Q学习算法改善了二分法。此方法通过逐步将状态空间和动作空间进行二分化来修改Q表的更新过程。仿真导致一个重复的Cournot游戏显示了拟议算法的有效性。
Q learning is widely used to simulate the behaviors of generation companies (GenCos) in an electricity market. However, existing Q learning method usually requires numerous iterations to converge, which is time-consuming and inefficient in practice. To enhance the calculation efficiency, a novel Q learning algorithm improved by dichotomy is proposed in this paper. This method modifies the update process of the Q table by dichotomizing the state space and the action space step by step. Simulation results in a repeated Cournot game show the effectiveness of the proposed algorithm.
