In batch reinforcement learning, there can be poorly explored state-action
pairs resulting in poorly learned, inaccurate models and poorly performing
associated policies. Various regularization methods can mitigate the problem of
learning overly-complex models in Markov decision processes (MDPs), however
they operate in technically and intuitively distinct ways and lack a common
form in which to compare them. This paper unifies three regularization methods
in a common framework -- a weighted average transition matrix. Considering
regularization methods in this common form illuminates how the MDP structure
and the state-action pair distribution of the batch data set influence the
relative performance of regularization methods. We confirm intuitions generated
from the common framework by empirical evaluation across a range of MDPs and
data collection policies.

本篇论文研究了在批量强化学习中，如何利用加正则化的方法解决由于样本不足或过于复杂导致的模型准确度不高和性能不佳的问题，并通过实验验证了在相同的加正则化措施下，MDP 结构和数据集中状态 - 动作对的分布如何影响其性能表现。