Reinforcement Learning
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf
Moreover,inmanysituations,there exist prediction intervals which result in particularly easy-to-predict transitions.
Total stochastic gradient algorithms and applications in reinforcement learning
Neural Information Processing Systems http://nips.cc/
Near-OptimalRegretforAdversarialMDPwith DelayedBanditFeedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observedindelay.
Near-OptimalRegretforAdversarialMDPwith DelayedBanditFeedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observedindelay.
