Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble. (arXiv:2205.09284v1 [cs.LG])

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 …

arxiv efficiency ensemble learning policy reinforcement reinforcement learning