Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching

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 …

algorithms application cognitive cognitive science community cs.ai cs.cv cs.lg cs.ne cs.ro current environments human human-like introduction natural networks performance q-learning reinforcement reinforcement learning robot science sensory success work