Learning Hill-Climbing Functions as a Strategy for Generating Behaviors in a Mobile Robot

Abstract

We consider the problem of a robot with uninterpreted sensors and effectors which must learn, in an unknown environment, behaviors (i.e., sequences of actions) which can be taken to achieve a given goal. This general problem involves a learning agent interacting with a reactive environment: the agent produces actions that affect the environment and in turn receives sensory feedback from the environment. The agent must learn, through experimentation, behaviors that consistently achieve the goal. The difficulty lies in the fact that the robot does not know a priori what its sensors mean, nor what effects its motor apparatus has on the world. We propose a method by which the robot may analyze its sensory information in order to derive (when possible) a function defined in terms of the sensory data which is maximized at the goal and which is suitable for hillclimbing. Given this function, the robot solves its problem by learning a behavior that maximizes the function thereby resulting in m...