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{Multi-Agent Deep Reinforcement Learning for Autonomous Energy Coordination in Demand Response Methods for Residential Distribution Networks
DownloadFall 2023
In the field of collaborative learning and decision-making, this thesis aims to explore the effects of individual and joint rewards on the performance and coordination of agents in complex environments. The research objectives encompass two main aspects: firstly, to determine the objective...
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1991
Pelletier, Francis J., Schubert, Lenhart
Introduction: This very short book is apparently intended as a supplementary text in a graduate AI course. The author describes it as a \"text and reference work on the applications of non-standard logics to artificial intelligence (AI).\" It gives short and concise (too short and too concise, in...
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[Review of the book Formal Methods in Artificial Intelligence, by Aamsay]
1996
Introduction: Many universities teach artificial intelligence (AI) by having one undergraduate course that introduces students to a very wide variety of topics, usually including search and search heuristics, representational systems (including formal logic), problem solving, vision, expert...
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Fall 2019
In this thesis, we investigate different vector step-size adaptation approaches for continual, online prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad,...
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Fall 2015
Extensive-form games are a powerful framework for modeling sequential multi-agent interactions. In extensive-form games with imperfect information, Nash equilibria are generally used as a solution concept, but computing a Nash equilibrium can be intractable in large games. Instead, a variety of...
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Spring 2016
Game theoretic solution concepts, such as Nash equilibrium strategies that are optimal against worst case opponents, provide guidance in finding desirable autonomous agent behaviour. In particular, we wish to approximate solutions to complex, dynamic tasks, such as negotiation or bidding in...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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Spring 2019
Juan Fernando Hernandez Garcia
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, the TD(λ) algorithm elegantly unifies temporal difference (TD) methods with Monte Carlo methods through the use of eligibility...