Comparing Learning Classifier System and Reinforcement Learning with Function Approximation
نویسندگان
چکیده
منابع مشابه
Reinforcement Learning and Function Approximation
Relational reinforcement learning combines traditional reinforcement learning with a strong emphasis on a relational (rather than attribute-value) representation. Earlier work used relational reinforcement learning on a learning version of the classic Blocks World planning problem (a version where the learner does not know what the result of taking an action will be). “Structural” learning resu...
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To fully understand the properties of Accuracy-based Learning Classifier Systems, we need a formal framework that captures all components of classifier systems, that is, function approximation, reinforcement learning, and classifier replacement, and permits the modelling of them separately and in their interaction. In this paper we extend our previous work on function approximation [22] to rein...
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A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that these algorithms can easily become unstable when implemented directly with a general function-approximation system, such as a sigmoidal multilayer perceptron, a radial-basisfunction system, a memory-based learning syst...
متن کاملFunction Approximation in Hierarchical Relational Reinforcement Learning
Recently there have been a number of dif ferent approaches developed for hierarchi cal reinforcement learning in propositional setting We propose a hierarchical version of relational reinforcement learning HRRL We describe a value function approximation method inspired by logic programming which is suitable for HRRL
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ژورنال
عنوان ژورنال: IEEJ Transactions on Electronics, Information and Systems
سال: 2004
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss.124.2034