Evolutionary algorithms and reinforcement learning in experiments with slot cars

Abstract

Some control systems are difficult or impossible to be tuned by other means than automatically. We present here examples of optimization of the parameters of a PID controller regulating velocity of a slot car to the given set point using evolutionary optimization and reinforcement learning. These methods are implemented on the micro-controller of the slot car. Experimental results and comparison are provided.