Neural Policy Style Transfer

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer …

arts concept control cs.ai cs.lg cs.ne cs.ro fields infrastructure language language processing natural natural language natural language processing network policy processing reinforcement reinforcement learning scale style style transfer transfer