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This study enhances a Deep Q-Network (DQN) trading model by incorporatingadvanced techniques like Prioritized Experience Replay, Regularized Q-Learning,Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USDand AAPL demonstrate superior performance compared to the original model, withmarked increases in returns and Sharpe Ratio, indicating improved risk-adjustedrewards. Notably, convolutional neural network (CNN) architectures, both 1D and2D, significantly boost returns, suggesting their effectiveness in market trendanalysis. Across instruments, these enhancements have yielded stable and highgains, eclipsing the baseline and highlighting the potential of CNNs in tradingsystems. The study suggests that applying sophisticated deep learning withinreinforcement learning can greatly enhance automated trading, urging furtherexploration into advanced methods for broader financial applicability. Thefindings advocate for the continued evolution of AI in finance.

Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models