Discretizing Continuous Action Space for On-Policy Optimization
نویسندگان
چکیده
منابع مشابه
An Efficient Method for Discretizing Continuous Attributes
In this paper the authors present a novel method for finding optimal split points for discretization of continuous attributes. Such a method can be used in many data mining techniques for large databases. The method consists of two major steps. In the first step search space is pruned using a bisecting region method that partitions the search space and returns the point with the highest informa...
متن کاملDiscretizing the State Space for High-Dimensional Continuous-State Stochastic Dynamic Programs
This paper describes a state space discretization scheme based on statistical experimental designs generated from orthogonal arrays of strength three with index unity. Chen et al. (1997) used this eecient discretization scheme to approximately solve high-dimensional continuous-state stochastic dynamic programming (SDP). Prior methods discretized the state space with a nite grid. The orthogonal ...
متن کاملDiscretizing Continuous Attributes Using Information Theory
Many classification algorithms require that training examples contain only discrete values. In order to use these algorithms when some attributes have continuous numeric values, the numeric attributes must be converted into discrete ones. This paper describes a new way of discretizing numeric values using information theory. The amount of information each interval gives to the target attribute ...
متن کاملDiscretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values for the discretization while learning the Bayesian network structure. This score balances the complexity of ...
متن کاملDiscretizing Continuous Features for Naive Bayes and C4.5 Classifiers
In this work, popular discretization techniques for continuous features in data sets are surveyed, and a new one based on equal width binning and error minimization is introduced. This discretization technique is implemented for the UCI Machine Learning Repository [7] dataset, Adult database and tested on two classifiers from WEKA tool [6], NaiveBayes and J48. Relative performance changes for t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i04.6059