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Spring 2023
Choosing an appropriate action representation is an integral part of solving robotic manipulation problems. Published approaches include latent action models, which train context-conditioned neural networks to map lowdimensional latent actions to high-dimensional actuation commands. Such models...
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Spring 2013
Gradient-TD methods are a new family of learning algorithms that are stable and convergent under a wider range of conditions than previous reinforcement learning algorithms. In particular, gradient-TD algorithms enable off-policy problems---problems where the distribution of the data is different...
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Fall 2010
Non-Player Character (NPC) behaviors in today’s computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial...
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Fall 2022
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Early detection and treatment are critical for improving this situation. Today's wearable medical devices are becoming popular because of their price and ease of use. Many manufacturers...
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Fall 2022
Machine learning has been used to solve single-agent search problems. One of its applications is to guide search algorithms by learning heuristics. However, it is difficult to provide guarantees on the quality of learning from a neural network, since the resulting heuristics can be inadmissible,...
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Spring 2020
Two-Player alternate-turn perfect-information zero-sum games have been suggested as a testbed for Artificial Intelligence research since Shannon in 1950s. In this thesis, we summarize and develop algorithms for this line of research. We focus on the game of Hex — a game created by Piet Hein in...