A Wise Up Visual Robot Driven by a Self-taught Neural Agent

Abstract

This paper presents a biological inspired robot capable of learning by itself high level Tic-Tac-Toe playing policies and then use this knowledge to advantageously compete with humans. The robot comprises a robotic arm, an artificial vision system and a self-motivated neural agent which has the capability to explore in a simulated ambient, new forms of game episodes that conduce toward bigger rewards. During the training phase a three terms reinforcement learning scheme is proposed, where the agent memory resources are sustained by adviser neural sub-networks, noise-balanced trained as to satisfy the look for future conditions in the control optimization predicted by the Bellman equation. In the operating phase the components merge into a wised up robot, with look ahead capacities, that mimic the abilities of ingenious human players. The achieved look ahead robotic intelligence could be useful in other complex robotic mechanisms.