Search Results for author: Anke Schmeink

Found 2 papers, 0 papers with code

EVO-RL: Evolutionary-Driven Reinforcement Learning

no code implementations9 Jul 2020 Ahmed Hallawa, Thorsten Born, Anke Schmeink, Guido Dartmann, Arne Peine, Lukas Martin, Giovanni Iacca, A. E. Eiben, Gerd Ascheid

Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments.

OpenAI Gym reinforcement-learning

Variational Network Quantization

no code implementations ICLR 2018 Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, Tim Genewein

In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem.

Quantization Variational Inference

Cannot find the paper you are looking for? You can Submit a new open access paper.