6 citations · 11 across the 6 of their papers we have counts for
6 papers
Human-level Atari 200x faster
Steven Kapturowski, Víctor Campos, Ray Jiang +4
The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subj…
Selective Credit Assignment
Veronica Chelu, Diana Borsa, Doina Precup +1
Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings. We describe a unified view on temporal-difference algorithms…
Chaining Value Functions for Off-Policy Learning
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its e…
Self-Consistent Models and Values
Gregory Farquhar, Kate Baumli, Zita Marinho +4
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment. In particular, models enable planning,…
Learning Expected Emphatic Traces for Deep RL
Ray Jiang, Shangtong Zhang, Veronica Chelu +2
Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approxima…
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu +4
Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms ca…