Reinforcement learning_hierarchical neuro-fuzzy politree model for control of autonomous agents

This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). This new model, named reinforcement learning hierarchical neuro-fuzzy politree (RL-HNFP), descends from the reinforcement learing hierarchical neuro-fuzzy BSP (RL-HNFB) that uses binary space partitioning. 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 represent an important differential when compared with the existing intelligent agents learning systems. The obtained results demonstrate the potential of this new model, which operates without any prior information, such as number of rules, rules specification, or number of partitions that the input space should have.