Transfer of human control strategy based on similarity measure
We address the problem of transferring human control strategies (HCS) from an expert model to an apprentice model. The proposed algorithm allows us to develop useful apprentice models that incorporate some of the robust aspects of the expert HCS models. We first describe our experimental platform, a real-time graphic driving simulator, for collecting and modeling human control strategies. Then, we discuss an adaptive neural network learning architecture for abstracting HCS models. Next, we define a hidden Markov model (HMM) based similarity measure which allows us to compare different human control strategies. This similarity measure is combined subsequently with simultaneously perturbed stochastic approximation to develop our proposed transfer learning algorithm. In this algorithm, an expert HCS model influences both the structure and the parametric representation of the eventual apprentice HCS model. Finally, we describe some experimental results of the proposed algorithm.