  It is challenging for reinforcement learning (RL) algorithms to succeed in
real-world applications like financial trading and logistic system due to the
noisy observation and environment shifting between training and evaluation.
Thus, it requires both high sample efficiency and generalization for resolving
real-world tasks. However, directly applying typical RL algorithms can lead to
poor performance in such scenarios. Considering the great performance of
ensemble methods on both accuracy and generalization in supervised learning
(SL), we design a robust and applicable method named Ensemble Proximal Policy
Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
Notably, EPPO combines each policy and the policy ensemble organically and
optimizes both simultaneously. In addition, EPPO adopts a diversity enhancement
regularization over the policy space which helps to generalize to unseen states
and promotes exploration. We theoretically prove EPPO increases exploration
efficacy, and through comprehensive experimental evaluations on various tasks,
we demonstrate that EPPO achieves higher efficiency and is robust for
real-world applications compared with vanilla policy optimization algorithms
and other ensemble methods. Code and supplemental materials are available at
https://seqml.github.io/eppo.
