3 papers
cs.LO2020
Learning Languages with Decidable Hypotheses
Julian Berger, Maximilian Böther, Vanja Doskoč +9
In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language. This so-called -index allows for naming arbitrary computably enume…
cs.LG2020
Maps for Learning Indexable Classes
Julian Berger, Maximilian Böther, Vanja Doskoč +9
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the language…
cs.LG2018
Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms
Winfried Lötzsch
Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state sp…