BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

1 code implementation • 3 Dec 2019 • Marek Rosa, Olga Afanasjeva, Simon Andersson, Joseph Davidson, Nicholas Guttenberg, Petr Hlubuček, Martin Poliak, Jaroslav Vítku, Jan Feyereisl

In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication.

Meta-Learning

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

1 code implementation • 20 Mar 2019 • Jaroslav Vítků, Petr Dluhoš, Joseph Davidson, Matěj Nikl, Simon Andersson, Přemysl Paška, Jan Šinkora, Petr Hlubuček, Martin Stránský, Martin Hyben, Martin Poliak, Jan Feyereisl, Marek Rosa

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations.

Model-based Reinforcement Learning

General AI Challenge - Round One: Gradual Learning

no code implementations • 17 Aug 2017 • Jan Feyereisl, Matej Nikl, Martin Poliak, Martin Stransky, Michal Vlasak

The General AI Challenge is an initiative to encourage the wider artificial intelligence community to focus on important problems in building intelligent machines with more general scope than is currently possible.