Adaptive robot learning in a non-stationary environment

Adaptive control is challenging in real-world applications such as robotics. Learning has to be rapid enough to be performed in real time and to avoid damage to the robot. Models using linear function approximation are interesting in such tasks because they offer rapid learning and have small memory and processing requirements. This makes them suitable as adaptive controllers in nonstationary environments, especially when the controller needs to be an embedded system. Experiments with a light-seeking robot illustrate how the robot adapts to the environment by Reinforcement Learning where the robot collects training samples by exploring the environment.