A Survey Analyzing Generalization in Deep Reinforcement Learning
Ezgi Korkmaz
Reinforcement learning research obtained significant success and attention
with the utilization of deep neural networks to solve problems in high
dimensional state or action spaces. While deep reinforcement learning policies
are currently being deployed in many different fields from medical applications
to self driving vehicles, there are still ongoing questions the field is trying
to answer on the generalization capabilities of deep reinforcement learning
policies. In this paper, we will outline the fundamental reasons why deep
reinforcement learning policies encounter overfitting problems that limit their
robustness and generalization capabilities. Furthermore, we will formalize and
unify the diverse solution approaches to increase generalization, and overcome
overfitting in state-action value functions. We believe our study can provide a
compact systematic unified analysis for the current advancements in deep
reinforcement learning, and help to construct robust deep neural policies with
improved generalization abilities.