Above are papers related to the following paper: [Paper_ID] Paper #168332. 	[Title] A COMPARISON OF Q-LEARNING AND CLASSIFIER SYSTEMS [Abstract] Reinforcement Learning is a class of problems in which an autonomous agent acting in a given environment improves its behavior by progressively maximizing a function calculated just on the basis of a succession of scalar responses received from the environment. Q-learning and classifier systems (CS) are two methods among the most used to solve reinforcement learning problems. Notwithstanding their popularity and their shared goal, they have been in the past often considered as two different models. In this paper we first show that the classifier system, when restricted to a sharp simplification called discounted max very simple classifier system (D MAX - VSCS), boils down to tabular Q-learning. It follows that D MAX -VSCS converges to the optimal policy as proved by Watkins & Dayan (1992), and that it can draw profit from the results of experimental and theoretical works dedicated to improve Q-learning and to facilitate its use in concrete applications. In the second part of the paper, we show that three of the restrictions we need to impose to the CS for deriving its equivalence with Q-learning, that is, no internal states, no don't care symbols, and no structural changes, turn out so essential as to be recently rediscovered and reprogrammed by Q-learning adepts. Eventually, we sketch further similarities among ongoing work within both research contexts. The main contribution of the paper is therefore to make explicit the strong similarities existing between Q-learning and classifier systems, and to show that experience gained with research within one domain can be useful to direct future research in the other one. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #18834. 	[Title] A Study of the Generalization Capabilities of XCS [Abstract] We analyze the generalization behavior of the XCS classifier system in environments in which only a few generalizations can be done. Experimental results presented in the paper evidence that the generalization mechanism of XCS can prevent it from learning even simple tasks in such environments. We present a new operator, named Specify, which contributes to the solution of this problem. XCS with the Specify operator, named XCSS, is compared to XCS in terms of performance and generalization capabilities in different types of environments. Experimental results show that XCSS can deal with a greater variety of environments and that it is more robust than XCS with respect to population size. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #86359. 	[Title] Evolving Optimal Populations with XCS Classifier Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133469. 	[Title] XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions more complex [Abstract] Wilson's recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations (representations); populations which accurately map all input/action pairs to payoff predictions using the smallest possible set of non-overlapping classifiers. The ability of XCS to evolve optimal populations for boolean multiplexer problems is demonstrated using condensation, a technique in which evolutionary search is suspended by setting the crossover and mutation rates to zero. Condensation is automatically triggered by self-monitoring of performance statistics, and the entire learning process is terminated by autotermination. Combined, these techniques allow a classifier system to evolve optimal representations of boolean functions without any form of supervision. Please summarise all the given information.

Paper #168332 has the title `A COMPARISON OF Q-LEARNING AND CLASSIFIER SYSTEMS  
` and the abstract `Reinforcement Learning is a class of problems in which an autonomous agent acting in a given environment improves its behavior by progressively maximizing a function calculated just on the basis of a succession of scalar responses received from the environment. Q-learning and classifier systems (CS) are two methods among the most used to solve reinforcement learning problems. Notwithstanding their popularity and their shared goal, they have been in the past often considered as two different models. In this paper we first show that the classifier system, when restricted to a sharp simplification called discounted max very simple classifier system (D MAX - VSCS), boils down to tabular Q-learning. It follows that D MAX -VSCS converges to the optimal policy as proved by Watkins & Dayan (1992), and that it can draw profit from the results of experimental and theoretical works dedicated to improve Q-learning and to facilitate its use in concrete applications. In the second part of the paper, we show that three of the restrictions we need to impose to the CS for deriving its equivalence with Q-learning, that is, no internal states, no don't care symbols, and no structural changes, turn out so essential as to be recently rediscovered and reprogrammed by Q-learning adepts. Eventually, we sketch further similarities among ongoing work within both research contexts. The main contribution of the paper is therefore to make explicit the strong similarities existing between Q-learning and classifier systems, and to show that experience gained with research within one domain can be useful to direct future research in the other one.
`.
Question: Which category should Paper #168332 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #168332 has the title `A COMPARISON OF Q-LEARNING AND CLASSIFIER SYSTEMS  
` and the abstract `Reinforcement Learning is a class of problems in which an autonomous agent acting in a given environment improves its behavior by progressively maximizing a function calculated just on the basis of a succession of scalar responses received from the environment. Q-learning and classifier systems (CS) are two methods among the most used to solve reinforcement learning problems. Notwithstanding their popularity and their shared goal, they have been in the past often considered as two different models. In this paper we first show that the classifier system, when restricted to a sharp simplification called discounted max very simple classifier system (D MAX - VSCS), boils down to tabular Q-learning. It follows that D MAX -VSCS converges to the optimal policy as proved by Watkins & Dayan (1992), and that it can draw profit from the results of experimental and theoretical works dedicated to improve Q-learning and to facilitate its use in concrete applications. In the second part of the paper, we show that three of the restrictions we need to impose to the CS for deriving its equivalence with Q-learning, that is, no internal states, no don't care symbols, and no structural changes, turn out so essential as to be recently rediscovered and reprogrammed by Q-learning adepts. Eventually, we sketch further similarities among ongoing work within both research contexts. The main contribution of the paper is therefore to make explicit the strong similarities existing between Q-learning and classifier systems, and to show that experience gained with research within one domain can be useful to direct future research in the other one.
`.
Question: Which category should Paper #168332 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Reinforcement_Learning