Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
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A Boolean Task Algebra For Reinforcement Learning
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A major challenge is thus in designing sample-efficient agents that can transfer their existing knowledge to solve new tasks quickly.
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LocalDifferentialPrivacyforRegretMinimizationin ReinforcementLearning
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We formulate this notion of privacy for RL by leveraging the local differential privacy(LDP)framework.
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4b32c2943a02331792877cc6b5205f49-Paper-Conference.pdf
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Deep learning algorithms have shown significant development thanks tothelargepre-collected dataset, such as SQuAD [47]innatural language processing (NLP), and ImageNet [4] in computer vision.
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