# Fuzzy rule interpolation and reinforcement learning

@article{Vincze2017FuzzyRI, title={Fuzzy rule interpolation and reinforcement learning}, author={D{\'a}vid Vincze}, journal={2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)}, year={2017}, pages={000173-000178} }

Reinforcement Learning (RL) methods became popular decades ago and still maintain to be one of the mainstream topics in computational intelligence. Countless different RL methods and variants can be found in the literature, each one having its own advantages and disadvantages in a specific application domain. Representation of the revealed knowledge can be realized in several ways depending on the exact RL method, including e.g. simple discrete Q-tables, fuzzy rule-bases, artificial neural…

## 16 Citations

### Antecedent Redundancy Exploitation in Fuzzy Rule Interpolation-based Reinforcement Learning

- Computer Science2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
- 2020

Novel methods which could improve the efficiency of the automated knowledge extraction methods used in the FRIQ-learning (Fuzzy Rule Interpolationbased Q-learning) machine learning method by facilitating the creation of a sparse fuzzy rule-base from which the knowledge can be directly extracted.

### Parallelization by Vectorization in Fuzzy Rule Interpolation Adapted to FRIQ-Learning

- Computer Science2018 World Symposium on Digital Intelligence for Systems and Machines (DISA)
- 2018

The goal was to identify parallelization possibilities and exploit fine-grained parallelism in FRIQ-learning by rearranging data structures and with using the Advanced Vector Extensions 2 (AVX2) SIMD instruction set present in most modern x86 architecture processors.

### Applying Expert Heuristic as an a Priori Knowledge for FRIQ-Learning

- Computer Science
- 2020

The main goal of this paper is the introduction of a methodology, suitable for merging the a priori stateaction fuzzy control rule-base to the initial state-action-value function (Q-function) representation.

### Expert heuristic tuning design for the FRIQ-learning

- Computer Science
- 2020

The main goal of this paper is to suggest a method for the FRIQ-learning system which may be suitable for optimizing the injected expert knowledgebase (Q-function) too, which is able to optimize (tune) the external knowledge rule-base during the learning phase too.

### Clustering-based fuzzy knowledgebase reduction in the FRIQ-learning

- Computer Science2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
- 2017

A new, clustering based reduction method is introduced, suitable for eliminating the unnecessary rules of the rule-base and hence decrease the size of the fuzzy knowledgebase.

### Tudásbázis redukció a szakértői szabályrendszerrel bővített FRIQ-learning módszerben

- Computer Science
- 2021

The main goal of this paper is to introduce a rule-base reduction strategy of the expert knowledgeincluded FRIQ-learning, which is able to reduce the rule- base size during the construction (learning) phase.

### SZAKÉRTŐI HEURISZTIKA ALKALMAZÁSA A FRIQ-LEARNING MEGERŐSÍTÉSES TANULÁSI MÓDSZERBEN

- Computer Science
- 2020

The main goal of the paper is to introduce the new developed version of the FRIQ-learning, which starts the learning phase with not an empty knowledgebase but with an expert-defined, a priori knowledgebase.

### Determining the minimally allowed rule-distance for the incremental rule-base contruction phase of the FRIQ-learning

- Computer Science2018 19th International Carpathian Control Conference (ICCC)
- 2018

A new rule-distance limit calculation methodology is introduced which can avoid the unnecessary high number of inserted rules in the initial phase of the iteration of the FRIQ-learning.

### Football Simulation Modeling with Fuzzy Rule Interpolation-based Fuzzy Automaton

- Computer Science2020 17th International Conference on Ubiquitous Robots (UR)
- 2020

The goal of this work was to construct such a model, which employs a human-readable knowledge representation to control the agents in a football simulation, which can be adapted to real robot hardware and also can be used as a reference model for fuzzy logic based machine learning methods.

### Smart Image-Processing based Energy Harvesting for Green Internet of Things

- Computer Science2018 Smart Grid Conference (SGC)
- 2018

A FQL-based approach that can maximize the lifetime of sensors and accelerate the process of wireless energy harvesting (EH) for mobile sensors which coexist with macro and small base stations deployed over a time-variant heterogeneous network (HetNet).

## References

SHOWING 1-10 OF 40 REFERENCES

### Incremental Rule Base Creation with Fuzzy Rule Interpolation-Based Q-Learning

- Computer Science
- 2010

A method which can construct the requested FRI fuzzy model from scratch in a reduced size is introduced, achieved by incremental creation of an intentionally sparse fuzzy rule base.

### Fuzzy Rule Interpolation-based Q-learning

- Computer Science2009 5th International Symposium on Applied Computational Intelligence and Informatics
- 2009

The main goal of this paper is to introduce Fuzzy Rule Interpolation (FRI), namely the F Five (Fuzzy rule Interpolations based on Vague Environment) to be the model applied with Q-learning (F RIQ-learning).

### Reduced Rule Base in Fuzzy Rule Interpolation-based Q-learning

- Computer Science
- 2009

The main goal of this paper is the introduction of a method which can construct the requested FRI fuzzy model in a reduced size by incremental creation of an intentionally sparse fuzzy rule base.

### Experience-based rule base generation and adaptation for fuzzy interpolation

- Computer Science2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
- 2016

This paper presents a novel rule base generation and adaptation system to allow the creation of rule bases with minimal a priori knowledge by adding accurate interpolated rules into the rule base guided by a performance index from the feedback mechanism.

### Extending the Fuzzy Rule Interpolation "FIVE" by Fuzzy Observation

- Computer Science
- 2006

The main contribution of this paper is the introduction of a way for handling fuzzy observations by extending the original “FIVE” concept with the ability of merging vague environments.

### Rule-base reduction in Fuzzy Rule Interpolation-based Q-learning

- Computer Science
- 2015

The goal of the paper is to introduce possible methods, which aim to find and remove the redundant and unnecessary rules from the rule-base automatically by using variations of newly developed decremental rule base reduction strategies.

### Reinforcement Learning: An Introduction

- Computer ScienceIEEE Transactions on Neural Networks
- 2005

This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

### Performance Optimization of the Fuzzy Rule Interpolation Method "FIVE"

- Computer ScienceJ. Adv. Comput. Intell. Intell. Informatics
- 2011

The goal of this paper is to introduce some implementation details of a low-computation and lowresource-demand FRI method, together with its brief time and space complexity analysis.

### A generalized concept for fuzzy rule interpolation

- Computer ScienceIEEE Transactions on Fuzzy Systems
- 2004

This paper proposes an interpolation methodology, whose key idea is based on the interpolation of relations instead of interpolating /spl alpha/-cut distances, and which offers a way to derive a family of interpolation methods capable of eliminating some typical deficiencies of fuzzy rule interpolation techniques.

### Extracting symbolic knowledge from recurrent neural networks - A fuzzy logic approach

- Computer ScienceFuzzy Sets Syst.
- 2009