Robot Reinforcement Learning Based on LCS-GDM

2013 ◽  
Vol 347-350 ◽  
pp. 416-420
Author(s):  
Yong Bin Ma
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Ability ◽  
Learning Classifier System ◽  
Learning Space ◽  
Rule Based ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
Learning Rules

This paper proposed a robot reinforcement learning method based on learning classifier system. A learning Classifier System is a rule-based machine learning system that combines reinforcement learning and genetic algorithms. The reinforcement learning component is responsible for adjusting the strength of rules in the system according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better learning rules. The advantages of this approach are its rule-based representation, which can be easily reduce learning space, online learning ability, robustness .

Multi-Robot Path Planning Based on Learning Classifier System with Policy Gradient Reinforcement Learning and Support Vector Machine (PGRL-SVM)

2013 ◽  
Vol 347-350 ◽  
pp. 3208-3211
Author(s):  
Qiu Li Song ◽  
Jian Bao Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Search Space ◽  
Learning System ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Classifier System ◽  
Learning Space ◽  
Classifier System ◽  
Learning Classifier

This paper presented a novel approach to solving the problem of robot avoidance collision planning. A Learning Classifier System is a accuracy-based machine learning system using gradient descent that combines covering operator and genetic algorithm. The covering operator is responsible for adjusting precision and large search space according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better path planning rules. The advantages of this approach are its accuracy-based representation, that can be easily reduce learning space,online learning ability,robustness due to the use of genetic algorithm.

Is a Learning Classifier System a Type of Neural Network?

1994 ◽  
Vol 2 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Robert E. Smith ◽  
H. Brown Cribbs
Keyword(s):  
The Other ◽  
Adaptive Genetic Algorithm ◽  
Diverse Population ◽  
Distinguishing Characteristic ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
The Relationship

This paper suggests a simple analogy between learning classifier systems (LCSs) and neural networks (NNs). By clarifying the relationship between LCSs and NNs, the paper indicates how techniques from one can be utilized in the other. The paper points out that the primary distinguishing characteristic of the LCS is its use of a co-adaptive genetic algorithm (GA), where the end product of evolution is a diverse population of individuals that cooperate to perform useful computation. This stands in contrast to typical GA/NN schemes, where a population of networks is employed to evolve a single, optimized network. To fully illustrate the LCS/NN analogy used in this paper, an LCS-like NN is implemented and tested. The test is constructed to run parallel to a similar GA/NN study that did not employ a co-adaptive GA. The test illustrates the LCS/NN analogy and suggests an interesting new method for applying GAs in NNs. Final comments discuss extensions of this work and suggest how LCS and NN studies can further benefit each other.

scholarly journals Self-adaptation of parameters in a learning classifier system ensemble machine

2010 ◽  
Vol 20 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Maciej Troć ◽  
Olgierd Unold
Keyword(s):  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
Ensemble Machine Learning ◽  
One Step ◽  
Self Adaptation ◽  
Selection Of ◽  
Self Adaptive

Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.

XCSR Learning from Compressed Data Acquired by Deep Neural Network

2017 ◽  
Vol 21 (5) ◽  
pp. 856-867 ◽  
Author(s):  
Kazuma Matsumoto ◽  
Takato Tatsumi ◽  
Hiroyuki Sato ◽  
Tim Kovacs ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Classifier System ◽  
Rule Based ◽  
Classifier System ◽  
Learning Classifier ◽  
The Neural Network ◽  
Compressed Data

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

scholarly journals A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 271
Author(s):  
Sulemana Nantogma ◽  
Yang Xu ◽  
Weizhi Ran
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
Hybrid Approach ◽  
Learning System ◽  
Artificial Immune ◽  
Learning Classifier System ◽  
Air Defense ◽  
Classifier System ◽  
Defense Systems ◽  
Learning Classifier

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of Learning Classifier System and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier system and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

scholarly journals Customer satisfaction prediction with Michigan-style learning classifier system

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Keivan Borna ◽  
Shokoofeh Hoseini ◽  
Mohammad Ali Mehdi Aghaei
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Adaptive System ◽  
Learning System ◽  
Classification Algorithms ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Genetic Base ◽  
Base Machine

Abstract Many different classification algorithms can be use in order to analyze, classify and predict data. Learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population’s accuracy and micro/macro population size. The result specifies features that mostly influence prediction.

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