Neural networks for self-learning control systems
Commonly used machine learning algorithms require a set of training examples comprised of both a hypothetical input and a desired output, and often the desired answer is an action for the robot to take. The robot can, at best, receive a value indicating the success or failure of a given action taken. One solution to this sort of problem framework is evolutionary algorithms, where the fitness function need only encode the success or failure of a given controller, rather than precise action. Algorithms in evolutionary robotics (ER) often work on populations of candidate controllers, initially selected from some distribution. This population is then repeatedly modified according to a fitness function. In genetic algorithms, the population of candidate controllers is repeatedly grown according to crossover, mutation and other operators and then culled according to the fitness function. The candidate controllers in ER can be taken from a subset of the set of artificial neural networks. Evolutionary robotics has may objectives, including creating useful controllers for real-world robots tasks, exploring the intricacies of evolutionary theory, reproducing psychological phenomena, and finding out about biological neural networks by studying artificial ones.
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