摘要翻译:
本文提出了一种新的强化学习方法,该方法是基于建模中一个强大的概念&主动学习方法(ALM)。ALM将任意多输入单输出系统表示为若干单输入单输出系统的模糊组合。该方法是一种类似于基于广义近似推理的智能控制(GARIC)结构的行为-批评系统,通过延迟强化信号来适应ALM。我们的系统使用时间差分(TD)学习来建模一个控制系统的有用动作的行为。一个行为的好是建立在奖罚平面上的。IDS飞机将根据此飞机进行更新。结果表明,该系统可以在预先定义的模糊系统中进行学习,也可以在没有预先定义的模糊系统的情况下进行学习(通过随机动作)。
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英文标题:
《Reinforcement Learning Based on Active Learning Method》
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作者:
Hesam Sagha, Saeed Bagheri Shouraki, Hosein Khasteh, and Ali Akbar
Kiaei
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最新提交年份:
2010
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward- Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the system can learn with a predefined fuzzy system or without it (through random actions).
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PDF链接:
https://arxiv.org/pdf/1011.1660