424 citations · 546 across the 10 of their papers we have counts for
22 papers
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,…
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…
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…
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhongwen Xu, Hado van Hasselt, Matteo Hessel +3
Deep reinforcement learning includes a broad family of algorithms that parameterise an internal representation, such as a value function or policy, by a deep neural network. Each a…
Discovering Reinforcement Learning Algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki +4
Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research. Automating the discov…