A parallel fuzzy inference model with distributed prediction scheme for reinforcement learning

This paper proposes a three-layered parallel fuzzy inference model called reinforcement fuzzy neural network with distributed prediction scheme (RFNN-DPS), which performs reinforcement learning with a novel distributed prediction scheme, In RFNN-DPS, an additional predictor for predicting the external reinforcement signal is mot necessary, and the internal reinforcement information is distributed into fuzzy rules (rule nodes), Therefore, using RFNN-DPS, only one network is needed to construct a fuzzy logic system with the abilities of parallel inference and reinforcement learning, Basically, the information for prediction in RFNN-DPS is composed of credit values stored in fuzzy rule nodes, where each node holds a credit vector to represent the reliability of the corresponding fuzzy rule, The credit values are not only accessed for predicting external reinforcement signals, but also provide a more profitable internal reinforcement signal to each fuzzy rule itself, RFNN-DPS performs a credit-based exploratory algorithm to adjust its internal status according to the internal reinforcement signal, During learning, the RFNN-DPS network is constructed by a single-step or multistep reinforcement learning algorithm based on the ART concept, According to our experimental results, RFNN-DPS shows the advantages of simple network structure,fast learning speed, and explicit representation of rule reliability.