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Skip to Search Results- 49Artificial Intelligence
- 27Machine Learning
- 25Natural Language Processing
- 19Planning
- 14Reinforcement Learning
- 7Deep Learning
- 1Akbari, Mojtaba
- 1Alexander, Graham
- 1Asadi Atui, Kavosh
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
- 1Atrazhev, Peter
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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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Vision-assisted behavior-based construction safety: Integrating computer vision and natural language processing
DownloadFall 2023
Background: Construction sites can be hazardous places. Behavior-based safety is a method to optimize workers’ behaviors and improve site safety. Previous behavior-based safety has been criticized for their low efficiency because of manual observation. The community has conducted enormous studies...
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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...