Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

We explore the benefits of augmenting state-of-the-art model-free deep reinforcement algorithms with simple object representations. Following the Frostbite challenge posited by Lake et al. (2017), we identify object representations as a critical cognitive capacity lacking from current reinforcement learning agents. We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representations substantially boosts its performance on the Frostbite game from Atari 2600. We then analyze the relative contributions of the representations of different types of objects, identify environment states where these representations are most impactful, and examine how these representations aid in generalizing to novel situations

Manufacturing and Testing of Ceramic Based Hybrid Composites with Various Fibers, Matrix Systems, and Processing Conditions.

M.S. Thesis. University of Hawaiʻi at Mānoa 2018

Public Private Partnerships for Economic Development: Impacts on Shrinking Cities

Over the last few decades in the United States, Public Private Partnerships have become essential devices to city development and redevelopment. This study examines the uses of Public Private Partnerships within the discipline of Economic Development. The cities researched are located in the Rust Belt region of the United States and their economies have experienced a drastic decline over the last half-century. This research reveals the economic impacts on these shrinking cities that have had programs and projects implemented through the agreements and workings of the public and private sectors. Several factors are analyzed to determine what constitutes the (un)successful and (un)satisfactory outcomes of these projects. Ultimately, improvements to these plans are suggested that could be enacted to ensure they are as sustainable and equitable as possible