Scenario co-evolution for reinforcement learning on a grid world smart factory domain-Reference-Cited by-同舟云学术

Scenario co-evolution for reinforcement learning on a grid world smart factory domain

Published:2019-07-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Gabor Thomas¹,Sedlmeier Andreas¹,Kiermeier Marie¹,Phan Thomy¹,Henrich Marcel²,Pichlmair Monika²,Kempter Bernhard³,Klein Cornel³,Sauer Horst³,AG Reiner SchmidSiemens³,Wieghardt Jan³

Affiliation:

1. LMU Munich

2. University of Augsburg

3. Siemens AG

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3321707.3321831

Reference54 articles.

1. An orchestrated survey of methodologies for automated software test case generation

2. Thomas Anthony Zheng Tian and David Barber. 2017. Thinking fast and slow with deep learning and tree search. In Advances in Neural Information Processing Systems. 5360--5370. Thomas Anthony Zheng Tian and David Barber. 2017. Thinking fast and slow with deep learning and tree search. In Advances in Neural Information Processing Systems. 5360--5370.

3. A novel co-evolutionary approach to automatic software bug fixing

4. Trapit Bansal Jakub Pachocki Szymon Sidor Ilya Sutskever and Igor Mordatch. 2018. Emergent Complexity via Multi-Agent Competition. In ICLR. Trapit Bansal Jakub Pachocki Szymon Sidor Ilya Sutskever and Igor Mordatch. 2018. Emergent Complexity via Multi-Agent Competition. In ICLR.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents;Entropy;2021-04-13

2. The scenario coevolution paradigm: adaptive quality assurance for adaptive systems;International Journal on Software Tools for Technology Transfer;2020-03-06