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Fall 2023
With the increasing complexity and capacity of modern Field-Programmable Gate Arrays (FPGAs), there is a growing demand for efficient FPGA computer-aided design (CAD) tools, particularly at the placement stage. While some previous works, such as RLPlace, have explored the efficacy of single-state...
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Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...
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Fall 2023
In reinforcement learning (RL), agents learn to maximize a reward signal using nothing but observations from the environment as input to their decision making processes. Whether the agent is simple, consisting of only a policy that maps observations to actions, or complex, containing auxiliary...
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Chasing Hallucinated Value: A Pitfall of Dyna Style Algorithms with Imperfect Environment Models
DownloadSpring 2020
In Dyna style algorithms, reinforcement learning (RL) agents use a model of the environment to generate simulated experience. By updating on this simulated experience, Dyna style algorithms allow agents to potentially learn control policies in fewer environment interactions than agents that use...
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Fall 2023
Off-policy policy evaluation has been a critical and challenging problem in reinforcement learning, and Temporal-Difference (TD) learning is one of the most important approaches for addressing it. There has been significant interest in searching for off-policy TD algorithms which find the same...
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Fall 2021
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benets to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather...
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Fall 2023
Multilevel action selection is a reinforcement learning technique in which an action is broken into two parts, the type and the parameters. When using multilevel action selection in reinforcement learning, one must break the action space into multiple subsets. These subsets are typically disjoint...
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Fall 2016
Current medical imaging professional training uses an apprenticeship model with students following an established doctor and viewing their cases, in what is called a practicum. This posses an issue as students are limited to the cases available during their practicum. To resolve this automated...
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Data-Driven and Artificial Intelligence Approach to Dynamic Truck Fleet Dispatching and Shovel Allocation Planning in Open-Pit Mines
DownloadFall 2023
An open-pit mine is a highly dynamic environment where different equipment resources are allocated to mining areas to extract metal-bearing rock and waste, for pit development, following a set flow of activities. The material mined is then transported through the mine road network to different...
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Spring 2024
Retrofitting buildings and optimizing their operation have been at the forefront of global efforts to reduce carbon emissions over the past few decades. Intelligent control of building systems, such as Heating, Ventilation, and Air Conditioning (HVAC), presents two clear benefits: it improves...