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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Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian
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We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.
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AcceleratingReinforcementLearningthroughGPU AtariEmulation
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CuLE is available at https://github.com/NVlabs/cule.
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WideningthePipelineinHuman-Guided ReinforcementLearningwithExplanationand Context-AwareDataAugmentation
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Human explanation (e.g.,interms offeature importance) hasbeenrecently used to extend the communication channel between human and agent in interactive machinelearning.
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