A Study on Overfitting in Deep Reinforcement Learning

Understand

Recent years have witnessed significant progresses in deep Reinforcement Learning (RL).

• Empowered with large scale neural networks, carefully designed architectures, novel training algorithms and massively parallel computing devices, researchers are able to attack many challenging RL problems.
• However, in machine learning, more training power comes with a potential risk of more overfitting.
• As deep RL techniques are being applied to critical problems such as healthcare and finance, it is important to understand the generalization behaviors of the trained agents.