Synthetic minority oversampling for function approximation problems
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22120
Abstract: Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). However, imbalanced data… read more here.
Keywords: function approximation; approximation; approximation problems; minority oversampling ... See more keywords
An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method
Sign Up to like & getrecommendations! Published in 2018 at "Machine Learning"
DOI: 10.1007/s10994-018-5727-z
Abstract: In this paper, we provide two new stable online algorithms for the problem of prediction in reinforcement learning, i.e., estimating the value function of a model-free Markov reward process using the linear function approximation architecture… read more here.
Keywords: function approximation; method; reinforcement learning; function ... See more keywords
Dynamic Spectrum Anti-Jamming With Reinforcement Learning Based on Value Function Approximation
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3228045
Abstract: This letter addresses the spectrum anti-jamming problem with multiple Internet of Things (IoT) devices for uplink transmissions, where policies for configuring frequency-domain channels have to be learned without the knowledge of the time-frequency distribution of… read more here.
Keywords: value function; reinforcement learning; function approximation; spectrum anti ... See more keywords
Using Reinforcement Learning to Control Traffic Signals in a Real-World Scenario: An Approach Based on Linear Function Approximation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2021.3091014
Abstract: Reinforcement learning is an efficient, widely used machine learning technique that performs well in problems with a reasonable number of states and actions. This is rarely the case regarding control-related problems, as for instance controlling… read more here.
Keywords: function approximation; inline formula; linear function; tex math ... See more keywords