论文标题
积极的加强学习 - 通往好奇分类器系统的路线图
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation
论文作者
论文摘要
智能系统可以随着时间的推移提高其行为，并考虑到观察，经验或明确的反馈。传统方法将学习问题分开，并与机器学习不同领域的技术孤立使用，例如增强学习，主动学习，异常检测或转移学习。在这种情况下，基本的强化学习方法带有几个缺点，阻碍了它们对现实世界系统的应用：反复试验，纯粹的反应性行为或孤立的问题处理。本文的想法是提出一个概念，可以通过为我们在智能系统中称为“主动强化学习”的研究议程来减轻这些缺点。
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 to real-world systems: trial-and-error, purely reactive behaviour or isolated problem handling. The idea of this article is to present a concept for alleviating these drawbacks by setting up a research agenda towards what we call "active reinforcement learning" in intelligent systems.
