Analysis of Temporal-Difference Learning

Control of Multivariable Systems Based on Emotional Temporal Difference Learning Controller

One of the most important issues that we face in controlling delayed systems and non-minimum phase systems is to fulfill objective orientations simultaneously and in the best way possible. In this paper proposing a new method, an objective orientation is presented for controlling multi-objective systems. The principles of this method is based an emotional temporal difference learning, and has a...

Insights in reinforcement rearning : formal analysis and empirical evaluation of temporal-difference learning algorithms

Crop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images

Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional ...

The Effect of Alpha-Lipoic Acid on Learning and Memory Deficit in a Rat Model of Temporal Lobe Epilepsy

Introduction: Epilepsy is a chronic neurological disorder in which patients experience spontaneous recurrent seizures and deficiency in learning and memory. Although the most commonly recommended therapy is drug treatment, some patients do not achieve adequate control of their seizures on existing drugs. New medications with novel mechanisms of action are needed to help those patients whose sei...

Some Simulation Results for Emphatic Temporal-Difference Learning Algorithms

This is a companion note to our recent study of the weak convergence properties of constrained emphatic temporal-difference learning (ETD) algorithms from a theoretic perspective. It supplements the latter analysis with simulation results and illustrates the behavior of some of the ETD algorithms using three example problems.

On a convergent off -policy temporal difference learning algorithm in on-line learning environment

In this paper we provide a rigorous convergence analysis of a “off”-policy temporal difference learning algorithm with linear function approximation and per time-step linear computational complexity in “online” learning environment. The algorithm considered here is TDC with importance weighting introduced by Maei et al. We support our theoretical results by providing suitable empirical results ...