Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading
This addresses stock trading automation for investors, but appears incremental as it integrates existing techniques without clear breakthrough claims.
The paper tackles automated stock trading by proposing a deep learning framework that combines CNN, LSTM, and DQN to learn optimal trading policies, achieving unspecified performance improvements over traditional methods.
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 Convolutional Neural Network (CNN) to identify patterns in technical indicators formatted as images, a Long Short-Term 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.