# A Stock Market Decision-Making Framework Based on CMR-DQN
## 1 Introduction
Reinforcement learning (RL), particularly its advanced branch known as deep reinforcement learning (DRL), has emerged as a leading technology for solving sequential decision-making problems by simulating the learning mechanism to identify optimal action strategies through continuous interaction with the environment [6]. DRL's notable breakthroughs, such as AlphaGo's victory over the World Go Champion, have validated its capability to handle complex decision-making challenges. Compared to classical stochastic control methods, RL offers innovative approaches that leverage information through iterative interactions between the agent and the system. The financial industry has seen numerous successes with RL algorithms in areas like order execution, market making, and portfolio optimization, generating significant interest and accelerating the development of RL technologies. This progress aims to enhance trading decisions in various financial markets, particularly when participants have limited information about the market and other competitors. In recent years, deep learning and reinforcement learning technologies have made remarkable breakthroughs and successes in many fields; however, there are still many challenges in applying these cutting-edge technologies to the field of financial trading, especially in the development of efficient trading algorithms. The high degree of uncertainty in financial markets, the non-stationary nature of data, and the need to make precise decisions in a very short period of time have greatly increased the complexity of designing and implementing effective trading strategies. In addition, an ideal financial trading system not only needs to pursue maximum returns but also fully considers the importance of risk control in the strategy to ensure that the trading strategy adopted has a high degree of robustness and reliability [7]. The recent study by [PERSON] [8] sheds light on strategic decision-making and dynamics in competitive environments, which are directly relevant to financial markets. By integrating these insights, this manuscript can better position itself within the broader context of economics and finance research, increasing its appeal to a wider audience. Despite these challenges, initial progress has been made in the application of deep reinforcement learning (DRL) techniques, especially Deep Q networks (DQNs) [9] and its variants, in financial transactions. Researchers began to try to use DQN technology to model and analyze the financial market and designed intelligent algorithms to find and use trading opportunities in the market automatically. The goal of these algorithmsis to maximize return on investment and Sharpe ratios intelligently in a volatile market environment, thereby improving the overall performance of trading strategies [10]. DQN and its variants make decisions directly from high-dimensional raw data by learning optimal action strategies, which provides a new perspective for analysis and forecasting in financial markets. Especially when working with financial time series data, DQN is able to identify and exploit hidden patterns and trends in historical information to predict future market dynamics. Compared with traditional financial analysis methods, this model-based learning method can more flexibly adapt to market changes and provide more accurate and stable trading decision support. The main contribution of this paper is to propose an innovative deep reinforcement learning (DRL) algorithm, the Composite Multi-Role Deep Q Network (CMR-DQN), which is designed to improve the accuracy and efficiency of stock market forecasting and decision-making. By combining the discrete wavelet transform (DWT), the temporal convolutional network (TCN), and the GRU-LSTM-Attention mechanism, CMR-DQN optimizes the processing power of traditional deep neural networks (DNNs) in many aspects, especially in the ability to parse complex financial time series data.