Two Classifier Systems for Reinforcement Learning of Motion Patterns

Abstract Two reinforcement learning technique for adaptive segmentation of the continuous state space have been formulated in this paper. The first technique uses weight vectors and continuous matching function, while the second technique makes use of Bayesian discrimination method, where the segmented state space is represented by Bayes boundaries. The proposed techniques have been tested under simulation for a navigation task, where the agent does not have a priori knowledge of the environment and its own internal model. The simulation results show that the agent can segment its continuous state space adaptively and reach the goal.