Action Recognition from Experience
Peter Tu, Thomas B. Sebastian, Dashan Gao · 2012
A reinforcement learning model, which allows for an agent to interact with a simulated 3D learning environment under the initial guidance of an all knowing oracle is proposed. Methods are presented that allow the agent to learn how to perform a set of task oriented actions. It is then hypothesized that the ability to recognize an action may in fact be a byproduct of learning how to perform an action. Evidence supporting this conjecture is presented using both simulated and real world imagery.