570 citations · 2.6k across the 40 of their papers we have counts for
8 papers · 1 filter
The Uncertainty Bellman Equation and Exploration
Brendan O'Donoghue, Ian Osband, Remi Munos +1
We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the…
A Tutorial on Thompson Sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni +2
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immed…
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…
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
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…