We use a connectionist network trained with rein-
forcement to control both an autonomous robot ve-
hicle and a simulated robot. We show that given
appropriate sensory data and architectural struc-
ture, a network can learn to control the robot for
a simple navigation problem. We then investigate a
more complex goal-based problem and examine the
plan-like behavior that emerges.
