most citedUsing Deep Reinforcement Learning for the Continuous Control of Robotic Arms

2 citations · 2 across the 1 of their papers we have counts for

collaborators

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★ 2 cited

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…