A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
This addresses the brittleness of deep RL methods for practitioners, but it is incremental as it builds on existing supervised learning concepts.
The paper tackles the problem of overfitting in deep reinforcement learning, particularly in continuous domains, by examining its definition, diagnosis, and prevention through training diversity, offering practical insights for researchers.
The risks and perils of overfitting in machine learning are well known. However most of the treatment of this, including diagnostic tools and remedies, was developed for the supervised learning case. In this work, we aim to offer new perspectives on the characterization and prevention of overfitting in deep Reinforcement Learning (RL) methods, with a particular focus on continuous domains. We examine several aspects, such as how to define and diagnose overfitting in MDPs, and how to reduce risks by injecting sufficient training diversity. This work complements recent findings on the brittleness of deep RL methods and offers practical observations for RL researchers and practitioners.