Browsing Computer Science and Automation (CSA) by Advisor "Bhatnagar, Shalabh"
Now showing items 1-20 of 23
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Algorithms for Challenges to Practical Reinforcement Learning
Reinforcement learning (RL) in real world applications faces major hurdles - the foremost being safety of the physical system controlled by the learning agent and the varying environment conditions in which the autonomous ... -
Algorithms for Product Pricing and Energy Allocation in Energy Harvesting Sensor Networks
(2018-05-09)In this thesis, we consider stochastic systems which arise in different real-world application contexts. The first problem we consider is based on product adoption and pricing. A monopolist selling a product has to appropriately ... -
Algorithms For Stochastic Games And Service Systems
(2014-04-23)This thesis is organized into two parts, one for my main area of research in the field of stochastic games, and the other for my contributions in the area of service systems. We first provide an abstract for my work in ... -
Algorithms for Stochastic Optimization, Statistical Estimation and Markov Decision Processes
Stochastic approximation deals with the problem of finding zeros of a function expressed as an expectation of a random variable. In this thesis we propose convergent algorithms for problems in optimization, statistical ... -
Approximate Dynamic Programming and Reinforcement Learning - Algorithms, Analysis and an Application
(2018-08-13)Problems involving optimal sequential making in uncertain dynamic systems arise in domains such as engineering, science and economics. Such problems can often be cast in the framework of Markov Decision Process (MDP). ... -
Decision Making under Uncertainty : Reinforcement Learning Algorithms and Applications in Cloud Computing, Crowdsourcing and Predictive Analytics
In this thesis, we study both theoretical and practical aspects of decision making, with a focus on reinforcement learning based methods. Reinforcement learning (RL) is a form of semi-supervised learning in which the agent ... -
Feature Adaptation Algorithms for Reinforcement Learning with Applications to Wireless Sensor Networks And Road Traffic Control
(2017-09-20)Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent ... -
Model-based Safe Deep Reinforcement Learning and Empirical Analysis of Safety via Attribution
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps, which in the real-world limit the practicality of these algorithms ... -
A Nonlinear Stochastic Optimization Framework For RED
(2011-09-23) -
Novel Reinforcement Learning Algorithms and Applications to Hybrid Control Design Problems
The thesis is a compilation of two independent works. In the first work, we develop novel weight assignment procedure, which helps us develop several schedule based algorithms. Learning the value function of a given policy ... -
On Generalized Measures Of Information With Maximum And Minimum Entropy Prescriptions
(2008-01-29)Kullback-Leibler relative-entropy or KL-entropy of P with respect to R defined as ∫xlnddPRdP , where P and R are probability measures on a measurable space (X, ), plays a basic role in the definitions of classical information ... -
Online Learning and Simulation Based Algorithms for Stochastic Optimization
(2018-03-07)In many optimization problems, the relationship between the objective and parameters is not known. The objective function itself may be stochastic such as a long-run average over some random cost samples. In such cases ... -
Online Optimization Of RED Routers
(2011-04-25) -
Optimization Algorithms for Deterministic, Stochastic and Reinforcement Learning Settings
(2018-05-30)Optimization is a very important field with diverse applications in physical, social and biological sciences and in various areas of engineering. It appears widely in ma-chine learning, information retrieval, regression, ... -
Reinforcement Learning Algorithms for Off-Policy, Multi-Agent Learning and Applications to Smart Grids
Reinforcement Learning (RL) algorithms are a popular class of algorithms for training an agent to learn desired behavior through interaction with an environment whose dynamics is unknown to the agent. RL algorithms ... -
Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation
Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also ... -
Resource Allocation for Sequential Decision Making Under Uncertainaty : Studies in Vehicular Traffic Control, Service Systems, Sensor Networks and Mechanism Design
(2017-11-27)A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated ... -
Scalable Sprase Bayesian Nonparametric and Matrix Tri-factorization Models for Text Mining Applications
(2018-05-23)Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component corn-responds ... -
Simulation Based Algorithms For Markov Decision Process And Stochastic Optimization
(2010-08-06)In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes (MDPs) with finite state-space under the average cost criterion. On the ... -
Stochastic Approximation Algorithms with Set-valued Dynamics : Theory and Applications
(2018-07-05)Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ...