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Skip to Search Results- 19Planning
- 5Reinforcement Learning
- 4Heuristic Search
- 3Artificial Intelligence
- 3Scheduling
- 1Abstraction
- 1Asadi Atui, Kavosh
- 1Brown, Jennifer A.
- 1Faid, Julian TW
- 1Fan, Gaojian
- 1Jabbari Arfaee, Shahab
- 1Kumar,Chandan
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...
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Fall 2012
The earthwork operations for reclamation add challenges and complications to common earthworks schedule and aspects such as placement locations and hauling routes…etc. The reclamation earthworks require that the soil layers structure before disturbing the land must remain the same after...
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On Efficient Planning in Large Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
DownloadFall 2023
A practical challenge in reinforcement learning is large action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement learning, a potentially large number of agents jointly optimize a global reward function, which leads to a blow-up in the...
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Fall 2018
Canada has the third largest oil reserves in the world where 97% of these reserves are located in the oil sands, Alberta province. The product resulted from the extraction of oil sands reserves is called bitumen which can be diluted and shipped to the market or it can be proceeded and upgraded...
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Planning for the Future of Urban Mobility: Interviews with Planning Professionals in Five Major Canadian Cities
DownloadFall 2020
Given that our urban centres have been dominated by the private car for a hundred years, this thesis asked what is next for Canadian cities. Previous research on the future of urban mobility, and specifically city planning and autonomous vehicles, has been from an American or Australian context....
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Spring 2014
Retail areas within cities have traditionally not only satisfied the demands for various goods and services, but also promoted community sustainability and healthy lifestyles. Since the end of World War II (WWII), retail innovations have occurred rapidly and unexpectedly. In retail development,...
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Social Actor Engagement in Municipal Decision-Making for Parks, Planning, and Civil Society in Edmonton, Alberta, Canada 1960-2010: Institutional Intersections
DownloadSpring 2020
Edmonton, Alberta, has a unique approach to public spaces that sees conjoined creation and development sharing of public spaces for the collective benefit of the community and stakeholders; this approach began 100 years ago. Green or open spaces, natural areas, the river valley, City of Edmonton...
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Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning
DownloadSpring 2016
Reinforcement learning algorithms are conventionally divided into two approaches: a model-based approach that builds a model of the environment and then computes a value function from the model, and a model-free approach that directly estimates the value function. The first contribution of this...
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Spring 2023
AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in the games of chess, shogi, and Go via policy iteration. To be an effective policy improvement operator, AlphaZero’s search needs to have accurate value estimates for the states that appear in its search...
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Fall 2019
In this thesis, we study merge-and-shrink (M&S), a flexible abstraction technique for generating heuristics for cost optimal planning. We first propose three novel merging strategies for M&S, namely, Undirected Min-Cut (UMC), Maximum Intermediate Abstraction Size Minimizing (MIASM), and Dynamic...