  Deep reinforcement learning is an effective tool to learn robot control
policies from scratch. However, these methods are notorious for the enormous
amount of required training data which is prohibitively expensive to collect on
real robots. A highly popular alternative is to learn from simulations,
allowing to generate the data much faster, safer, and cheaper. Since all
simulators are mere models of reality, there are inevitable differences between
the simulated and the real data, often referenced as the 'reality gap'. To
bridge this gap, many approaches learn one policy from a distribution over
simulators. In this paper, we propose to combine reinforcement learning from
randomized physics simulations with policy distillation. Our algorithm, called
Distilled Domain Randomization (DiDoR), distills so-called teacher policies,
which are experts on domains that have been sampled initially, into a student
policy that is later deployed. This way, DiDoR learns controllers which
transfer directly from simulation to reality, i.e., without requiring data from
the target domain. We compare DiDoR against three baselines in three sim-to-sim
as well as two sim-to-real experiments. Our results show that the target domain
performance of policies trained with DiDoR is en par or better than the
baselines'. Moreover, our approach neither increases the required memory
capacity nor the time to compute an action, which may well be a point of
failure for successfully deploying the learned controller.
