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Ensembling Diverse Policies Improves Generalization of Deep Reinforcement Learning Algorithms to Environmental Changes in Continuous Control Tasks
DownloadFall 2023
Deep Reinforcement Learning (DRL) algorithms have shown great success in solving continuous control tasks. However, they often struggle to generalize to changes in the environment. Although retraining may help policies adapt to changes, it may be quite costly in some environments. Ensemble...
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Fall 2024
Training large language models (LLMs) often requires extensive human supervision and struggles with modeling long-range text semantic dependencies. To address these challenges, we introduce our framework ELITE — Evolving Language models Iteratively Through self-critiquE — inspired by human...
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Spring 2023
Recent advancements in large language models and program synthesis have enabled the development of powerful programming assistance tools. These tools are designed to help the programmer while writing a program in an online setting. In this thesis we introduce a programming assistant that can...
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Fall 2022
Some real-world deployments of deep reinforcement learning (RL) may require a human-in-the-loop. Whether to ask-for-help, obtain new demonstrations and data, or handle out-of-distribution states, many methods rely on uncertainty estimates from a neural network to determine when to solicit a...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...