Reinforcement Learning tasks are learning problems where the desired behavior is not known; only sparse feedback on how well the agent is doing is provided. Reinforcement Learning techniques include value-function and policy iteration methods (note that although evolutionary computation and neuroevolution can also be seen as reinforcement learning methods, they are presented separately in this area hierarchy). Our research in reinforcement learning includes real-world applications such as packet routing, satellite communication, robot navigation, and sensorimotor embedding.