  Ensemble methods have been widely applied in Reinforcement Learning (RL) in
order to enhance stability, increase convergence speed, and improve
exploration. These methods typically work by employing an aggregation mechanism
over actions of different RL algorithms. We show that a variety of these
methods can be unified by drawing parallels from committee voting rules in
Social Choice Theory. We map the problem of designing an action aggregation
mechanism in an ensemble method to a voting problem which, under different
voting rules, yield popular ensemble-based RL algorithms like Majority Voting
Q-learning or Bootstrapped Q-learning. Our unification framework, in turn,
allows us to design new ensemble-RL algorithms with better performance. For
instance, we map two diversity-centered committee voting rules, namely Single
Non-Transferable Voting Rule and Chamberlin-Courant Rule, into new RL
algorithms that demonstrate excellent exploratory behavior in our experiments.
