  Ensemble learning is a very prevalent method employed in machine learning.
The relative success of ensemble methods is attributed to their ability to
tackle a wide range of instances and complex problems that require different
low-level approaches. However, ensemble methods are relatively less popular in
reinforcement learning owing to the high sample complexity and computational
expense involved in obtaining a diverse ensemble. We present a novel training
and model selection framework for model-free reinforcement algorithms that use
ensembles of policies obtained from a single training run. These policies are
diverse in nature and are learned through directed perturbation of the model
parameters at regular intervals. We show that learning and selecting an
adequately diverse set of policies is required for a good ensemble while
extreme diversity can prove detrimental to overall performance. Selection of an
adequately diverse set of policies is done through our novel policy selection
framework. We evaluate our approach on challenging discrete and continuous
control tasks and also discuss various ensembling strategies. Our framework is
substantially sample efficient, computationally inexpensive and is seen to
outperform state-of-the-art (SOTA) scores in Atari 2600 and Mujoco.
