Hierarchical Neuro-fuzzy Models Based on Reinforcement Learning for Intelligent Agents

Abstract

This work introduces two new neuro-fuzzy systems for intelligent agents called Reinforcement Learning – Hierarchical Neuro-Fuzzy Systems BSP (RL-HNFB) and Reinforcement Learning – Hierarchical Neuro-Fuzzy Systems Politree (RL-HNFP). By using hierarchical partitioning methods, together with the Reinforcement Learning (RL) methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent. These characteristics have been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems. The paper details the two novel RL_HNF systems and evaluates their performance in a benchmark application – the cart-centering problem. The results obtained demonstrate the capacity of the proposed models in extracting knowledge from the agent’s direct interaction with large and/or continuous environments.