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Fall 2022

Liu, Puer

We have witnessed the rising popularity of real-world applications of reinforcement learning (RL). However, most successful real-world applications of RL rely on high-fidelity simulators that enable rapid iteration of prototypes, hyperparameter selection and policy training. On the other hand, RL...

Fall 2024

Mohammad Panahi, Parham

Experience replay, the reuse of past data to improve sample efficiency, is ubiquitous in reinforcement learning. Though a variety of smart sampling schemes have been introduced to improve performance, uniform sampling by far remains the most common approach. One exception is Prioritized...

Fall 2024

Mesbahi, Golnaz

If we aspire to design algorithms that can run for long periods, continually adapting to new, unexpected situations, then we must be willing to deploy our agents without tuning their hyperparameters over the agent’s entire lifetime. The standard practice in deep RL—and even continual RL—is to...

Fall 2023

Schlegel, Matthew K

Partial observability---when the senses lack enough detail to make an optimal decision---is the reality of any decision making agent acting in the real world. While an agent could be made to make due with its available senses, taking advantage of the history of senses can provide more context and...

Fall 2021

Sakhadeo, Archit

The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous,...

Spring 2022

Sina Ghiassian

In this dissertation, we study online off-policy temporal-difference learning algorithms, a class of reinforcement learning algorithms that can learn predictions in an efficient and scalable manner. The contributions of this dissertation are one of the two kinds: (1) empirically studying existing...

Fall 2023

Pramanik, Subhojeet

The transformer architecture is effective in processing sequential data, both because of its ability to leverage parallelism, and because of its self-attention mechanism capable of capturing long-range dependencies. However, the self-attention mechanism is slow for streaming data, that is when...

Fall 2019

Jacobsen, Andrew

In this thesis, we investigate different vector step-size adaptation approaches for continual, online prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad,...