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Active Reinforcement Learning – A Roadmap Towards Curious Classifier Systems for Self-Adaptation
[article]
2022
arXiv
pre-print
Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the learning problem and make isolated use of techniques from different field of machine learning such as reinforcement learning, active learning, anomaly detection or transfer learning, for instance. In this context, the fundamental reinforcement learning approaches come with several drawbacks that hinder their application
arXiv:2201.03947v1
fatcat:6dgept56bfg2bdiqvvlrlm6ha4