We propose an integrated learning by experience and demonstration algorithm that operates on the basis of both an objective scalar measure of performance and a demonstrated behaviour. The application of the method to two qualitatively different experimental scenarios involving simulated mobile robots demonstrates its efficacy. Indeed, the analysis of the obtained results shows that the robots trained through this integrated algorithm develop solutions that are functionally better than those obtained by using either a pure learning by demonstration, or a pure learning by experience algorithm. This is because the algorithm drives the learning process toward solutions that are qualitatively similar to the demonstration, but leaves the learning agent free to differentiate from the demonstration when this turns out to be necessary to maximize performance.
