  The current success of Reinforcement Learning algorithms for its performance
in complex environments has inspired many recent theoretical approaches to
cognitive science. Artistic environments are studied within the cognitive
science community as rich, natural, multi-sensory, multi-cultural environments.
In this work, we propose the introduction of Reinforcement Learning for
improving the control of artistic robot applications. Deep Q-learning Neural
Networks (DQN) is one of the most successful algorithms for the implementation
of Reinforcement Learning in robotics. DQN methods generate complex control
policies for the execution of complex robot applications in a wide set of
environments. Current art painting robot applications use simple control laws
that limits the adaptability of the frameworks to a set of simple environments.
In this work, the introduction of DQN within an art painting robot application
is proposed. The goal is to study how the introduction of a complex control
policy impacts the performance of a basic art painting robot application. The
main expected contribution of this work is to serve as a first baseline for
future works introducing DQN methods for complex art painting robot frameworks.
Experiments consist of real world executions of human drawn sketches using the
DQN generated policy and TEO, the humanoid robot. Results are compared in terms
of similarity and obtained reward with respect to the reference inputs
