This study investigates the enhancement of the traditional Deep Q-Network (DQN) trader model through the integration of cutting-edge techniques such as Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling DQN, and Double DQN. Through rigorous empirical testing on a spectrum of financial instruments including BTC/USD and AAPL, the research delineates clear performance improvements over the original model. The augmented DQN trader showcases remarkable gains, with the DQN-vanilla variant achieving an arithmetic return increase from 261% to 287% and an enhanced Sharpe Ratio, indicative of better risk-adjusted returns. The innovative use of CNN1D and CNN2D architectures further amplifies returns, highlighting the efficacy of convolutional layers in capturing market dynamics. The enhanced model's consistency is evident in its application to AAPL stock, where substantial gains are observed. The DQN-pattern variant maintains a stable performance, while the CNN-based models demonstrate their architectural potency through exceptional returns. These results not only eclipse those of the baseline model but also underscore the potential of utilizing convolutional neural networks within financial trading systems. The study's findings confirm that the application of these sophisticated deep learning techniques within a reinforcement learning framework can significantly improve the performance of automated trading strategies. This performance consistency across various financial instruments underpins the importance of continued innovation in this domain. The research concludes by advocating for future exploration into new reinforcement learning methods and their potential to expand the model's effectiveness across a wider array of financial environments. The abstract encapsulates the core advancements and implications of the study, setting the stage for future developments in the realm of AI-driven financial trading.

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