  The past decade has seen the rapid development of Reinforcement Learning,
which acquires impressive performance with numerous training resources.
However, one of the greatest challenges in RL is generalization efficiency
(i.e., generalization performance in a unit time). This paper proposes a
framework of Active Reinforcement Learning (ARL) over MDPs to improve
generalization efficiency in a limited resource by instance selection. Given a
number of instances, the algorithm chooses out valuable instances as training
sets while training the policy, thereby costing fewer resources. Unlike
existing approaches, we attempt to actively select and use training data rather
than train on all the given data, thereby costing fewer resources. Furthermore,
we introduce a general instance evaluation metrics and selection mechanism into
the framework. Experiments results reveal that the proposed framework with
Proximal Policy Optimization as policy optimizer can effectively improve
generalization efficiency than unselect-ed and unbiased selected methods.
