451 citations · 1.7k across the 73 of their papers we have counts for
5 papers · 1 filter
Conservative Contextual Linear Bandits
Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi-Yadkori +1
Safety is a desirable property that can immensely increase the applicability of learning algorithms in real-world decision-making problems. It is much easier for a company to deplo…
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…
Deep Exploration via Bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel +1
Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computational…