Generalization in Transfer Learning

Understand

Agents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments.

• We investigate transferring the learning acquired in one task to a set of previously unseen tasks.
• Generalization and overfitting in deep reinforcement learning are not commonly addressed in current transfer learning research.
• Conducting a comparative analysis without an intermediate regularization step results in underperforming benchmarks and inaccurate algorithm comparisons due to rudimentary assessments.