252 citations · 1k across the 12 of their papers we have counts for
3 papers · 1 filter
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning
Adam Stooke, Valentin Dalibard, Siddhant M. Jayakumar +2
We introduce a new recurrent agent architecture and associated auxiliary losses which improve reinforcement learning in partially observable tasks requiring long-term memory. We em…
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Peter Sunehag, Guy Lever, Audrunas Gruslys +8
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large co…
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul +4
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan a…