Technical Note: \cal Q-Learning
Machine Learning
ML92 Proceedings of the ninth international workshop on Machine learning
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Adding temporary memory to ZCS
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Avatars!; Exploring and Building Virtual Worlds on the Internet
Avatars!; Exploring and Building Virtual Worlds on the Internet
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Computation
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Implicit Imitation in Multiagent Reinforcement Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Accelerating reinforcement learning through imitation
Accelerating reinforcement learning through imitation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Skill reconstruction as induction of LQ controllers with subgoals
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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In this paper, we study the means of developing an imitation process allowing to improve learning in the framework of learning classifier systems. We present three different approaches in the way a behavior observed may be taken into account through a guidance interaction: two approaches using a model of this behavior, and one without modelling. Those approaches are evaluated and compared in different environments when they are applied to three major classifier systems: ZCS, XCS and ACS. Results are analyzed and discussed. They highlight the importance of using a model of the observed behavior to enable an efficient imitation. Moreover, they show the advantages of taking this model into account by a specialized internal action. Finally, they bring new results of comparison between ZCS, XCS and ACS.