Overfitting in DRL

[AhdHazim]

Does there is possibility to DRL algorithm to face overfitting issues as the supervised learning does? if so then how could we check it and mitigate it?

I would be thankful for any information regarding this.

[gaikwadrahul8]

Hi, @AhdHazim

I apologize for the delayed response and as far I know Yes, DRL algorithms can suffer from overfitting though it manifests a bit differently than in supervised learning and overfitting in DRL, The agent learns a policy that works well in the specific training environment but fails to generalize to slightly different situations.This can happen due to limited training data overly complex models or focusing too much on initial experiences.

Monitor the agent's performance on a separate validation environment that resembles real-world scenarios. A large gap between training and validation performance suggests overfitting.

Mitigating Overfitting:

• Data Augmentation: Introduce variations in the training environment (e.g., randomizing starting positions, adding noise) to promote robustness.
• Experience Replay: Store past experiences in a buffer and sample from it during training. This helps the agent learn from a diverse set of situations.
• Regularization: Techniques like dropout (temporarily removing neurons during training) can prevent the model from overfitting to specific details.
• Early Stopping: Stop training when the validation performance starts to decrease.

Please refer official documentation of TensorFlow and tutorials which may help you to solve your overfitting issue.

If I have missed something here please let me know.

Thank you for your cooperation and patience.

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