1k citations · 1.1k across the 8 of their papers we have counts for
9 papers
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
Francesco Faccio, Aditya Ramesh, Vincent Herrmann +2
Learning to evaluate and improve policies is a core problem of Reinforcement Learning (RL). Traditional RL algorithms learn a value function defined for a single policy. A recently…
Policy Evaluation Networks
Jean Harb, Tom Schaul, Doina Precup +1
Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a single policy while generalizing across ma…
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
Jeanine Harb, Nicolas Rébéna, Raphaël Chosidow +3
In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation su…
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul, Hado van Hasselt, Joseph Modayil +7
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose o…
Learnings Options End-to-End for Continuous Action Tasks
Martin Klissarov, Pierre-Luc Bacon, Jean Harb +1
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]). In orderto achieve this goal w…
When Waiting is not an Option : Learning Options with a Deliberation Cost
Jean Harb, Pierre-Luc Bacon, Martin Klissarov +1
Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of "how" to learn optio…