424 citations · 1.3k across the 16 of their papers we have counts for
8 papers · 1 filter
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…
Hindsight Credit Assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard +8
We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit…
Conditional Importance Sampling for Off-Policy Learning
Mark Rowland, Anna Harutyunyan, Hado van Hasselt +4
The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This fram…
Adaptive Trade-Offs in Off-Policy Learning
Mark Rowland, Will Dabney, Rémi Munos
A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoretical understanding and yielding…
Fast Task Inference with Variational Intrinsic Successor Features
Steven Hansen, Will Dabney, Andre Barreto +3
It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from…
The Termination Critic
Anna Harutyunyan, Will Dabney, Diana Borsa +3
In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We propose an algorithm that focuses on th…