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Fall 2023

Chen, Ruichen

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...

Spring 2022

Garg, Shivam

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...

Fall 2024

May, Daniel Christopher

The conventional unidirectional model of the electricity grid operations is no longer sufficient. The continued proliferation of distributed energy resources and the resultant surge in net load variability at the grid edge necessitates deploying adequate demand response methods. This thesis...

Fall 2024

Coblin, Jordan Frederick

The sensitivity of reinforcement learning algorithm performance to hyperparameter choices poses a significant hurdle to the deployment of these algorithms in the real-world, where sampling can be limited by speed, safety, or other system constraints. To mitigate this, one approach is to learn a...

Fall 2024

Li, Yuxuan

Reinforcement learning has been widely applied in different control tasks. However, its performance often faces the challenge of low sample efficiency. Introducing human prior knowledge is often seen as a possible solution, such as behaviour cloning, learning from advice, and inverse...

Fall 2023

Meyer, Edan J

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...

Spring 2020

Jafferjee, Taher

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...

Fall 2023

He, Jiamin

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...

Fall 2021

McLeod, Matthew

Learning auxiliary tasks, such as multiple predictions about the world, can provide many benets 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...

Fall 2023

Mitchell, Daniel

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...