2k citations · 7.2k across the 38 of their papers we have counts for
4 papers · 2 filters
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017
M. Botvinick, D. G. T. Barrett, P. Battaglia +16
We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favo…
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt +7
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
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…