  Deep Reinforcement Learning (DRL) experiments are commonly performed in
simulated environments due to the tremendous training sample demands from deep
neural networks. In contrast, model-based Bayesian Learning allows a robot to
learn good policies within a few trials in the real world. Although it takes
fewer iterations, Bayesian methods pay a relatively higher computational cost
per trial, and the advantage of such methods is strongly tied to dimensionality
and noise. In here, we compare a Deep Bayesian Learning algorithm with a
model-free DRL algorithm while analyzing our results collected from both
simulations and real-world experiments. While considering Sim and Real
learning, our experiments show that the sample-efficient Deep Bayesian RL
performance is better than DRL even when computation time (as opposed to number
of iterations) is taken in consideration. Additionally, the difference in
computation time between Deep Bayesian RL performed in simulation and in
experiments point to a viable path to traverse the reality gap. We also show
that a mix between Sim and Real does not outperform a purely Real approach,
pointing to the possibility that reality can provide the best prior knowledge
to a Bayesian Learning. Roboticists design and build robots every day, and our
results show that a higher learning efficiency in the real-world will shorten
the time between design and deployment by skipping simulations.
