570 citations · 2.6k across the 40 of their papers we have counts for
4 papers · 2 filters
On Optimistic versus Randomized Exploration in Reinforcement Learning
Ian Osband, Benjamin Van Roy
We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimist…
Deep Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Daniel Russo +1
We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally…
Minimax Regret Bounds for Reinforcement Learning
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs. We show that an optimistic modification to value iteration achieves a reg…
Gaussian-Dirichlet Posterior Dominance in Sequential Learning
Ian Osband, Benjamin Van Roy
We consider the problem of sequential learning from categorical observations bounded in [0,1]. We establish an ordering between the Dirichlet posterior over categorical outcomes an…