424 citations · 437 across the 5 of their papers we have counts for
9 papers
The Difficulty of Passive Learning in Deep Reinforcement Learning
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on…
Return-based Scaling: Yet Another Normalisation Trick for Deep RL
Tom Schaul, Georg Ostrovski, Iurii Kemaev +1
Scaling issues are mundane yet irritating for practitioners of reinforcement learning. Error scales vary across domains, tasks, and stages of learning; sometimes by many orders of…
On The Effect of Auxiliary Tasks on Representation Dynamics
Clare Lyle, Mark Rowland, Georg Ostrovski +1
While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achie…
Temporally-Extended ε-Greedy Exploration
Will Dabney, Georg Ostrovski, André Barreto
Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expen…
Adapting Behaviour for Learning Progress
Tom Schaul, Diana Borsa, David Ding +4
Determining what experience to generate to best facilitate learning (i.e. exploration) is one of the distinguishing features and open challenges in reinforcement learning. The adve…
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN. We…