学习和行动的最小相对熵原理

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摘要翻译：
本文提出了一种构造自适应agent的方法，该自适应agent对给定的专家类具有通用性，其中每个专家都是专门为特定环境设计的agent。该自适应控制问题被形式化为最小化最适合未知环境的专家自适应智能体的相对熵问题。如果agent是被动观测者，那么最优解就是众所周知的贝叶斯预测器。但是，如果代理是活动的，那么它过去的动作需要被视为对I/O流的因果干预，而不是正常的概率条件。本文证明了这个新的变分问题的解是由一个称为贝叶斯控制规则的随机控制器给出的，该控制器以混合专家的形式实现自适应行为。进一步证明了在温和的假设下，贝叶斯控制规则收敛于最合适专家的控制律。
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英文标题：
《A Minimum Relative Entropy Principle for Learning and Acting》
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作者：
Pedro A. Ortega, Daniel A. Braun
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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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is an agent that has been designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a passive observer, then the optimal solution is the well-known Bayesian predictor. However, if the agent is active, then its past actions need to be treated as causal interventions on the I/O stream rather than normal probability conditions. Here it is shown that the solution to this new variational problem is given by a stochastic controller called the Bayesian control rule, which implements adaptive behavior as a mixture of experts. Furthermore, it is shown that under mild assumptions, the Bayesian control rule converges to the control law of the most suitable expert.
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PDF链接：
https://arxiv.org/pdf/0810.3605

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关键词：Presentation Intelligence intervention Applications environment 环境 形式 rule 规则 本文