9 citations · 21 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Podracer architectures for scalable Reinforcement Learning
Matteo Hessel, Manuel Kroiss, Aidan Clark +5
Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally asso…
Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel +6
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster. However, despite prior work on this topic, the problem of disc…
Discovering a set of policies for the worst case reward
Tom Zahavy, Andre Barreto, Daniel J Mankowitz +4
We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward func…