Advances in Reinforcement Learning and Their Implications

Advances in Reinforcement Learning and Their Implications for Intelligent Control

RRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features

Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...

Medical students’ academic emotions: the role of perceived learning environment

Introduction: Research shows that there is a relationship betweenstudents’ perceptions of classroom and learning environment andtheir cognitive, affective, emotional and behavioral outcomes, so,in this study the relationship between medical students’ perceptionof learning environment and academic emotions was examined.Methods: The research method used was descriptive-correlative.The statistical...

A Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem

Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...

Development of Reinforcement Learning Algorithm to Study the Capacity Withholding in Electricity Energy Markets

This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL...