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Fall 2022

Li, Xin

In this thesis, we investigate the empirical performance of several experience replay techniques. Efficient experience replay plays an important role in model-free reinforcement learning by improving sample efficiency through reusing past experience. However, replay-based methods were largely...

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 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...

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 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,...

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,...