A Study on Overfitting in Deep Reinforcement Learning

When Smart Programs Fail Outside Training: Overfitting in Deep Learning

New computer agents can learn fast, but sometimes they only learn the practice test, not the real world.
Researchers found these systems may look perfect during training yet fail badly later, and that is worrying for things like healthcare and finance.
The problem called overfitting means a model remembers details instead of learning rules, so it breaks when situations change.
Many tricks people use to make learning less certain, do not always catch this, so problems can hide.
That means two programs that scored the same while training, could give very different results outside, which is surprising.
We need better ways to check how well these systems will do on new tasks, because current tests can be misleading.
This work asks for smarter checks and careful thinking before using these models in important places, and hopes to push fixs that make them safer and fairer.
The future will be brighter if we test more, question more, and design for real change.

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A Study on Overfitting in Deep Reinforcement Learning

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