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.

通过利用深度神经网络解决高维状态或动作空间中的问题，强化学习研究在许多领域获得了显著的成功和关注。本文将概述深度强化学习策略遇到过拟合问题从而限制其鲁棒性和泛化能力的基本原因，并形式化和统一不同的解决方法来增加泛化能力并克服状态 - 动作值函数中的过拟合问题。我们相信我们的研究可以为当前深度强化学习的进展提供一个紧凑的系统统一分析，并有助于构建具有改进泛化能力的鲁棒深度神经策略。