Obstacle Avoidance through Reinforcement Learning

Abstract

A method is described for generating plan-like. reflexive. obstacle
\navoidance behaviour in a mobile robot. The experiments reported here
\nuse a simulated vehicle with a primitive range sensor. Avoidance
\nbehaviour is encoded as a set of continuous functions of the perceptual
\ninput space. These functions are stored using CMACs and trained by a
\nvariant of Barto and Sutton's adaptive critic algorithm. As the vehicle
\nexplores its surroundings it adapts its responses to sensory stimuli so
\nas to minimise the negative reinforcement arising from collisions.
\nStrategies for local navigation are therefore acquired in an explicitly
\ngoal-driven fashion. The resulting trajectories form elegant collisionfree
\npaths through the environment.