72 citations · 219 across the 19 of their papers we have counts for
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cs.LG2018★ 10 cited
Information-Directed Exploration for Deep Reinforcement Learning
Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp +1
Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is…
stat.ML2018★ 72 cited
Information Directed Sampling and Bandits with Heteroscedastic Noise
Johannes Kirschner, Andreas Krause
In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations. Typically, the observation noise is assumed to be independent of…