Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions

Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, existing accuracy-based learning systems either address primarily single-step learning problems or require the action space to be discrete. In this paper, a new accuracy-based learning fuzzy classifier system (LFCS) is developed to explicitly handle continuous state input and continuous action output during multistep reinforcement learning. Several technical improvements have been achieved while developing the new learning algorithm. Particularly, we have successfully extended Q-learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.