Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Inspired by this process, MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that affects how future … | Continue reading
Reinforcement Learning promises to solve the problem of designing intelligent agents in a formal but simple framework. This blog post aims at tackling the massive quantity of approaches and challenges in Reinforcement Learning, providing an overview of the different challenges re … | Continue reading
We look at Active Inference, a theoretical formulation of perception and action from neuroscience that can explain many phenomena in the brain. It aims to explain the behavior of cells, organs, animals, humans, and entire species. This article is geared towards machine learning r … | Continue reading
I give insight into a theoretic top-down approach of universal artificial intelligence. This may allow directing our future research by theoretical guidance, avoiding to handcraft properties of a system that may be learned by an intelligent agent. Furthermore, we will learn about … | Continue reading
In this blog post we want to look at the distributed computation framework ray and its little brother ray tune that allow distributed and easy to implement hyperparameter search. It not only supports population-based training, but also other hyperparameter search algorithms. Ray … | Continue reading