  Deep Reinforcement Learning (DRL) has become a powerful strategy to solve
complex decision making problems based on Deep Neural Networks (DNNs). However,
it is highly data demanding, so unfeasible in physical systems for most
applications. In this work, we approach an alternative Interactive Machine
Learning (IML) strategy for training DNN policies based on human corrective
feedback, with a method called Deep COACH (D-COACH). This approach not only
takes advantage of the knowledge and insights of human teachers as well as the
power of DNNs, but also has no need of a reward function (which sometimes
implies the need of external perception for computing rewards). We combine Deep
Learning with the COrrective Advice Communicated by Humans (COACH) framework,
in which non-expert humans shape policies by correcting the agent's actions
during execution. The D-COACH framework has the potential to solve complex
problems without much data or time required. Experimental results validated the
efficiency of the framework in three different problems (two simulated, one
with a real robot), with state spaces of low and high dimensions, showing the
capacity to successfully learn policies for continuous action spaces like in
the Car Racing and Cart-Pole problems faster than with DRL.
