Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

10 Feb 2017 Stefan Elfwing Eiji Uchibe Kenji Doya

In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games... (read more)

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