| A review on deep reinforcement learning for fluid mechanics P Garnier, J Viquerat, J Rabault, A Larcher, A Kuhnle, E Hachem Computers & Fluids 225, 104973, 2021 | 252 | 2021 |
| Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning H Tang, J Rabault, A Kuhnle, Y Wang, T Wang Physics of Fluids 32 (5), 2020 | 193 | 2020 |
| Direct shape optimization through deep reinforcement learning J Viquerat, J Rabault, A Kuhnle, H Ghraieb, A Larcher, E Hachem Journal of Computational Physics 428, 110080, 2021 | 190 | 2021 |
| Tensorforce: a TensorFlow library for applied reinforcement learning A Kuhnle, M Schaarschmidt, K Fricke Web page, 2017 | 181* | 2017 |
| Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach J Rabault, A Kuhnle Physics of Fluids 31 (9), 2019 | 174 | 2019 |
| ShapeWorld-A new test methodology for multimodal language understanding A Kuhnle, A Copestake arXiv preprint arXiv:1704.04517, 2017 | 73 | 2017 |
| Comparative analysis of machine learning methods for active flow control F Pino, L Schena, J Rabault, MA Mendez Journal of Fluid Mechanics 958, A39, 2023 | 51 | 2023 |
| LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations M Schaarschmidt, A Kuhnle, B Ellis, K Fricke, F Gessert, E Yoneki arXiv preprint arXiv:1808.07903, 2018 | 46 | 2018 |
| Resources for building applications with Dependency Minimal Recursion Semantics A Copestake, G Emerson, MW Goodman, M Horvat, A Kuhnle, ... Proceedings of the Tenth Language Resources and Evaluation Conference (LREC’16), 2016 | 33 | 2016 |
| A Proposition-Based Abstractive Summariser Y Fang, H Zhu, E Muszynska, A Kuhnle, S Teufel | 22* | |
| Policy-based optimization: single-step policy gradient method seen as an evolution strategy J Viquerat, R Duvigneau, P Meliga, A Kuhnle, E Hachem Neural Computing and Applications 35 (1), 449-467, 2023 | 20 | 2023 |
| Tensorforce: a tensorflow library for applied reinforcement learning (2017) A Kuhnle, M Schaarschmidt, K Fricke Available online: tensorforce. readthedocs. io (accessed on 21 December 2021), 2019 | 14 | 2019 |
| Deep learning evaluation using deep linguistic processing A Kuhnle, A Copestake arXiv preprint arXiv:1706.01322, 2017 | 12 | 2017 |
| Reinforcement Learning for Information Retrieval A Kuhnle, M Aroca-Ouellette, A Basu, M Sensoy, J Reid, D Zhang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 11 | 2021 |
| Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity H Xie, T Sherborne, A Kuhnle, A Copestake arXiv preprint arXiv:1912.08960, 2019 | 10 | 2019 |
| Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents A Sestini, A Kuhnle, AD Bagdanov 2021 IEEE Conference on Games (CoG), 01-08, 2021 | 9 | 2021 |
| Deep reinforcement learning applied to active flow control J Rabault, A Kuhnle ResearchGate Preprint https://doi. org/10.13140/RG 2 (10482.94404), 2020 | 7 | 2020 |
| How clever is the FiLM model, and how clever can it be? A Kuhnle, H Xie, A Copestake European Conference on Computer Vision, 162-172, 2018 | 7 | 2018 |
| DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games A Sestini, A Kuhnle, AD Bagdanov arXiv preprint arXiv:2012.01914, 2020 | 6 | 2020 |
| Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments A Sestini, A Kuhnle, AD Bagdanov arXiv preprint arXiv:2012.02527, 2020 | 4* | 2020 |
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