Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Fuzzy Sets and Systems - Special issue on diagnostics and control through neural interpretations of fuzzy sets
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hierarchical neuro-fuzzy quadtree models
Fuzzy Sets and Systems - Fuzzy models
Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Hierarchical neuro-fuzzy systems
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.