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We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient… Expand The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known… Expand We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input… Expand Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This… Expand Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning… Expand We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can… Expand This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing… Expand Objective—To evaluate the pharmacokinetics of a novel commercial formulation of ivermectin after administration to goats. Animals… Expand From the Publisher:
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key… Expand This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to… Expand