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Improving Different Aspects in RL - Accelerating Convergence Rate & Enhancing Safety and Robustness
DownloadFall 2021
Reinforcement learning (RL) has moved from toy domains to real-world applications, while each of these applications has inherent difficulties which are long-standing challenges in RL, such as: stucking at plateaus, limited training time, costly exploration and safety considerations. I, with my...
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Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
DownloadFall 2013
Significant research has gone into engineering representations that can identify high-level semantic structure in images, such as objects, people, events and scenes. Recently there has been a shift towards learning representations of images either on top of dense features or directly from the...
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...
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Fall 2016
In an online learning problem a player makes decisions in a sequential manner. In each round, the player receives some reward that depends on his action and an outcome generated by the environment while some feedback information about the outcome is revealed. The goal of the player can be...