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.