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Spring 2024

Patterson, Andrew

In this thesis, we seek to improve the reliability of reinforcement learning algorithms for nonlinear function approximation. Semi-gradient temporal difference (TD) update rules form the basis of most state-of-the-art value function learning systems despite clear counterexamples proving their...

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 2020

Bhatt, Varun S.

Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human communication become necessary. Ideally, these agents should be trained in an incremental and decentralized manner. In...

Fall 2022

Mohammadhasani, Arash

This thesis targets output tracking problem for payload position and quadrotor yaw in an slung load system (SLS). In spite of its relatively extensive literature, full SLS control is still a challenging problem since its dimension, nonlinearity, and multiple sources of disturbances are not easy...

Spring 2021

Wong, Andy

This thesis investigates the use of general value functions for detecting anomalous behavior in machines. Identifying abnormal behavior is critical for ensuring the safety and reliability of any machine or industrial process. When the cause of these anomalies is due to accumulated wear on...

Fall 2020

Chockalingam, Valliappa

The reinforcement learning framework provides a simple way to study computational intelligence as the interaction between an agent and an environment. The goal of an agent is to accrue as much reward as possible by intelligently choosing actions given states. This problem of finding a policy that...

Fall 2021

Yang, Bin

Powered by advancements of information and Internet technologies, there has been a rapid development in network based applications in recent years. Meanwhile, it is recognized that more attentions need to be paid to the issue of cybersecurity. The security of the network environment plays a vital...

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 2021

Farrahi, Homayoon

Continuous-time reinforcement learning tasks commonly use discrete time steps of fixed cycle times for actions. Choosing a small action-cycle time in such tasks allows reinforcement learning agents fast reaction and a more temporally detailed perception of the environment. The learning...

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...