| Batch reinforcement learning S Lange, T Gabel, M Riedmiller Reinforcement learning: State-of-the-art, 45-73, 2012 | 549 | 2012 |
| Reinforcement learning for robot soccer M Riedmiller, T Gabel, R Hafner, S Lange Autonomous Robots 27, 55-73, 2009 | 374 | 2009 |
| Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm A Tharwat, M Elhoseny, AE Hassanien, T Gabel, A Kumar Cluster Computing 22, 4745-4766, 2019 | 215 | 2019 |
| Adaptive reactive job-shop scheduling with reinforcement learning agents T Gabel, M Riedmiller International Journal of Information Technology and Intelligent Computing 24 …, 2008 | 117 | 2008 |
| Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm A Tharwat, T Gabel Neural computing and applications 32, 6925-6938, 2020 | 91 | 2020 |
| MOGOA algorithm for constrained and unconstrained multi-objective optimization problems A Tharwat, EH Houssein, MM Ahmed, AE Hassanien, T Gabel Applied Intelligence 48, 2268-2283, 2018 | 88 | 2018 |
| Using evolution programs to learn local similarity measures A Stahl, T Gabel ICCBR 2689, 537-551, 2003 | 83 | 2003 |
| On experiences in a complex and competitive gaming domain: Reinforcement learning meets robocup M Riedmiller, T Gabel 2007 IEEE Symposium on Computational Intelligence and Games, 17-23, 2007 | 82 | 2007 |
| Distributed policy search reinforcement learning for job-shop scheduling tasks T Gabel, M Riedmiller International Journal of production research 50 (1), 41-61, 2012 | 79 | 2012 |
| Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines A Tharwat, YS Moemen, AE Hassanien Journal of biomedical informatics 68, 132-149, 2017 | 76 | 2017 |
| A case study on improving defense behavior in soccer simulation 2D: The NeuroHassle approach T Gabel, M Riedmiller, F Trost RoboCup 2008: Robot Soccer World Cup XII 12, 61-72, 2009 | 61 | 2009 |
| D3. 1.1. a: KAON–ontology management infrastructure T Gabel, Y Sure, J Voelker SEKT informal deliverable, 2004 | 60 | 2004 |
| Cbr for state value function approximation in reinforcement learning T Gabel, M Riedmiller Case-Based Reasoning Research and Development: 6th International Conference …, 2005 | 54 | 2005 |
| On a successful application of multi-agent reinforcement learning to operations research benchmarks T Gabel, M Riedmiller 2007 IEEE international symposium on approximate dynamic programming and …, 2007 | 51 | 2007 |
| A biometric-based model for fish species classification A Tharwat, AA Hemedan, AE Hassanien, T Gabel Fisheries Research 204, 324-336, 2018 | 41 | 2018 |
| Multi-Agent Reinforcement Learning Approaches for Distributed Job-Shop Scheduling Problems T Gabel University of Osnabrueck, 2009 | 40 | 2009 |
| Parameter optimization of support vector machine using dragonfly algorithm A Tharwat, T Gabel, AE Hassanien Proceedings of the International Conference on Advanced Intelligent Systems …, 2018 | 39 | 2018 |
| Learning a Partial Behavior for a Competitive Robotic Soccer Agent. T Gabel, MA Riedmiller Künstliche Intell. 20 (2), 18-23, 2006 | 34 | 2006 |
| Brainstormers 2D–Team Description 2005 M Riedmiller, T Gabel, J Knabe, H Strasdat RoboCup, 219-229, 2005 | 33 | 2005 |
| Scaling adaptive agent-based reactive job-shop scheduling to large-scale problems T Gabel, M Riedmiller 2007 IEEE Symposium on Computational Intelligence in Scheduling, 259-266, 2007 | 30 | 2007 |
Sascha LangePSIORIVerified email at psiori.com
