Deep Reinforcement Learning (DRL) has obtained unprecedented results in decisionmaking problems, such as playing Atari games [1], or beating the world champion inGO [2]. 

Nevertheless, in robotic problems, DRL is still limited in applications with
real-world systems [3]. Most of the tasks that have been successfully addressed with
DRL have two common characteristics: 1) they have well-specified reward functions, and 2) they require large amounts of trials, which means long training periods
(or powerful computers) to obtain a satisfying behavior. These two characteristics
can be problematic in cases where 1) the goals of the tasks are poorly defined or
hard to specify/model (reward function does not exist), 2) the execution of many
trials is not feasible (real systems case) and/or not much computational power or
time is available, and 3) sometimes additional external perception is necessary for
computing the reward/cost function.

On the other hand, Machine Learning methods that rely on transfer of human
knowledge, Interactive Machine Learning (IML) methods, have shown to be time efficient for obtaining good performance policies and may not require a well-specified
reward function; moreover, some methods do not need expert human teachers for
training high performance agents [4–6]. In previous years, IML techniques were
limited to work with low-dimensional state spaces problems and to the use of function approximation such as linear models of basis functions (choosing a right basis
function set was crucial for successful learning), in the same way as RL. But, as
DRL have showed, by approximating policies with Deep Neural Networks (DNNs)
it is possible to solve problems with high-dimensional state spaces, without the need
of feature engineering for preprocessing the states. If the same approach is used in
IML, the DRL shortcomings mentioned before can be addressed with the support of
human users who participate in the learning process of the agent.
This work proposes to extend the use of human corrective feedback during task
execution to learn policies with state spaces of low and high dimensionality in continuous action problems (which is the case for most of the problems in robotics)
using deep neural networks.

We combine Deep Learning (DL) with the corrective advice based learning
framework called COrrective Advice Communicated by Humans (COACH) [6],
thus creating the Deep COACH (D-COACH) framework. In this approach, no reward functions are needed and the amount of learning episodes is significantly reduced in comparison to alternative approaches. D-COACH is validated in three different tasks, two in simulations and one in the real-world.