  Transferring learning-based models to the real world remains one of the
hardest problems in model-free control theory. Due to the cost of data
collection on a real robot and the limited sample efficiency of Deep
Reinforcement Learning algorithms, models are usually trained in a simulator
which theoretically provides an infinite amount of data. Despite offering
unbounded trial and error runs, the reality gap between simulation and the
physical world brings little guarantee about the policy behavior in real
operation. Depending on the problem, expensive real fine-tuning and/or a
complex domain randomization strategy may be required to produce a relevant
policy. In this paper, a Soft-Actor Critic (SAC) training strategy using
incremental environment complexity is proposed to drastically reduce the need
for additional training in the real world. The application addressed is
depth-based mapless navigation, where a mobile robot should reach a given
waypoint in a cluttered environment with no prior mapping information.
Experimental results in simulated and real environments are presented to assess
quantitatively the efficiency of the proposed approach, which demonstrated a
success rate twice higher than a naive strategy.
