98 citations · 339 across the 69 of their papers we have counts for
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The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in th…
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit +3
Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead i…
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in…
The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Ole-Christoffer Granmo
Although simple individually, artificial neurons provide state-of-the-art performance when interconnected in deep networks. Arguably, the Tsetlin Automaton is an even simpler and m…
FlashRL: A Reinforcement Learning Platform for Flash Games
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer game…