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Skip to Search Results- 85Artificial Intelligence
- 22Machine Learning
- 20Aggregation
- 10Reinforcement Learning
- 8Planning
- 7Computer Games
- 4Müller, Martin
- 3Mueller, Martin
- 2Johanson, Michael
- 2Nakhost, Hootan
- 2Pelletier, Francis J.
- 2Schaeffer, Jonathan
- 65Graduate and Postdoctoral Studies (GPS), Faculty of
- 65Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 19Computing Science, Department of
- 19Computing Science, Department of/Technical Reports (Computing Science)
- 4Toolkit for Grant Success
- 4WISEST Summer Research Program
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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 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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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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{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...