  Deep Reinforcement Learning has been able to achieve amazing successes in a
variety of domains from video games to continuous control by trying to maximize
the cumulative reward. However, most of these successes rely on algorithms that
require a large amount of data to train in order to obtain results on par with
human-level performance. This is not feasible if we are to deploy these systems
on real world tasks and hence there has been an increased thrust in exploring
data efficient algorithms. To this end, we propose the Shared Learning
framework aimed at making $Q$-ensemble algorithms data-efficient. For achieving
this, we look into some principles of transfer learning which aim to study the
benefits of information exchange across tasks in reinforcement learning and
adapt transfer to learning our value function estimates in a novel manner. In
this paper, we consider the special case of transfer between the value function
estimates in the $Q$-ensemble architecture of BootstrappedDQN. We further
empirically demonstrate how our proposed framework can help in speeding up the
learning process in $Q$-ensembles with minimum computational overhead on a
suite of Atari 2600 Games.
