570 citations · 2.6k across the 40 of their papers we have counts for
14 papers · 1 filter
Evaluating High-Order Predictive Distributions in Deep Learning
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3
Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…
Randomized Prior Functions for Deep Reinforcement Learning
Ian Osband, John Aslanides, Albin Cassirer
Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many…
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…
On Lower Bounds for Regret in Reinforcement Learning
Ian Osband, Benjamin Van Roy
This is a brief technical note to clarify the state of lower bounds on regret for reinforcement learning. In particular, this paper: - Reproduces a lower bound on regret for reinfo…
Posterior Sampling for Reinforcement Learning Without Episodes
Ian Osband, Benjamin Van Roy
This is a brief technical note to clarify some of the issues with applying the application of the algorithm posterior sampling for reinforcement learning (PSRL) in environments wit…
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Ian Osband, Benjamin Van Roy
Computational results demonstrate that posterior sampling for reinforcement learning (PSRL) dramatically outperforms algorithms driven by optimism, such as UCRL2. We provide insigh…
Bootstrapped Thompson Sampling and Deep Exploration
Ian Osband, Benjamin Van Roy
This technical note presents a new approach to carrying out the kind of exploration achieved by Thompson sampling, but without explicitly maintaining or sampling from posterior dis…
Model-based Reinforcement Learning and the Eluder Dimension
Ian Osband, Benjamin Van Roy
We consider the problem of learning to optimize an unknown Markov decision process (MDP). We show that, if the MDP can be parameterized within some known function class, we can obt…
Near-optimal Reinforcement Learning in Factored MDPs
Ian Osband, Benjamin Van Roy
Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer regret on some MDP, where is the elapsed time and and…
Generalization and Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Zheng Wen
We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized valu…
(More) Efficient Reinforcement Learning via Posterior Sampling
Ian Osband, Daniel Russo, Benjamin Van Roy
Most provably-efficient learning algorithms introduce optimism about poorly-understood states and actions to encourage exploration. We study an alternative approach for efficient e…