Browsing Division of Electrical, Electronics, and Computer Science (EECS) by Subject "Markov Decision Processes"
Now showing items 1-9 of 9
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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 ... -
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 ... -
Exploring Fairness and Causality in Online Decision-Making
Online decision-making under uncertainty is a fundamental aspect of numerous real-world problems across various domains, including online resource allocation, crowd-sourcing, and online advertising. Multi-Armed Bandits ... -
Multi-timescale and Multi-agent Reinforcement Learning Algorithms
This thesis presents six novel works involving several research domains, such as reinforcement learning (RL)– both with or without function approximators including deep neural networks, multi-agent RL, stochastic optimization, ... -
On Policy Gradients, Momentum, and Learning with Adversaries: Algorithms and Convergence Analysis
This thesis comprises five works, organized into three parts: the first focuses on average-reward Reinforcement Learning (RL), the second on distributed learning under adversaries in heterogeneous and asynchronous setups, ... -
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 ... -
Sequential Decision Making with Risk, Offline Data and External Influence: Bandits and Reinforcement Learning
Reinforcement Learning (RL) serves as a foundational framework for addressing sequential decision-making problems under uncertainty. In recent years, extensive research in this domain has led to significant advancements ... -
Single and Multi-Agent Finite Horizon Reinforcement Learning Algorithms for Smart Grids
In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ...