  This study enhances a Deep Q-Network (DQN) trading model by incorporating
advanced techniques like Prioritized Experience Replay, Regularized Q-Learning,
Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USD
and AAPL demonstrate superior performance compared to the original model, with
marked increases in returns and Sharpe Ratio, indicating improved risk-adjusted
rewards. Notably, convolutional neural network (CNN) architectures, both 1D and
2D, significantly boost returns, suggesting their effectiveness in market trend
analysis. Across instruments, these enhancements have yielded stable and high
gains, eclipsing the baseline and highlighting the potential of CNNs in trading
systems. The study suggests that applying sophisticated deep learning within
reinforcement learning can greatly enhance automated trading, urging further
exploration into advanced methods for broader financial applicability. The
findings advocate for the continued evolution of AI in finance.
