  Recent advances of gradient temporal-difference methods allow to learn
off-policy multiple value functions in parallel with- out sacrificing
convergence guarantees or computational efficiency. This opens up new
possibilities for sound ensemble techniques in reinforcement learning. In this
work we propose learning an ensemble of policies related through
potential-based shaping rewards. The ensemble induces a combination policy by
using a voting mechanism on its components. Learning happens in real time, and
we empirically show the combination policy to outperform the individual
policies of the ensemble.
