Deep Q-Networks (DQNs) leverage neural networks to improve reinforcement learning for trading, enhancing traditional Q-Learning by predicting future actions and rewards in complex, high-dimensional markets. Key advancements include using a neural network for mapping Q-values, enabling DQNs to handle dynamic environments and adapt to new data. The target network offers stability, reducing oscillations by periodically syncing with the main network. Experience Replay is employed to train DQNs with diverse, randomized environment samples, thus mitigating overfitting. These techniques help traders develop robust algorithmic strategies and adapt to the fast-changing financial market landscape.


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