Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading

This project addresses the challenge of automated stock trading, where traditional methods and direct reinforcement learning (RL) struggle with market noise, complexity, and generalization. Our proposed solution is an integrated deep learning framework combining a Convolu-tional Neural Network (CNN) to identify patterns in technical indicators formatted as images, a Long Short-T erm Memory (LSTM) network to capture temporal dependencies across both price history and technical indicators, and a Deep Q-Network (DQN) agent which learns the optimal trading policy (buy, sell, hold) based on the features extracted by the CNN and LSTM. The CNN and LSTM act as sophisticated feature extractors, feeding processed information to the DQN, which learns the optimal trading policy (buy, sell, hold) through RL. W e trained and evaluated this model on historical daily stock data, using distinct periods for training, testing, and validation. Performance was assessed by comparing the agent's returns and risk on out-of-sample test data against baseline strategies, including passive buy-and-hold approaches. This analysis, along with insights gained from explainability techniques into the agent's decision-making process, aimed to demonstrate the effectiveness of combining specialized deep learning architectures, document challenges encountered, and potentially uncover learned market insights.