Policy-based optimization: single-step policy gradient method seen as an evolution strategy. - Viquerat, Jonathan; Duvigneau, Régis; Meliga, P.; Kuhnle, Alexander; Hachem, Elie (Neural Comput. Appl., 2023)

Comparative analysis of machine learning methods for active flow control. - Pino, Fabio; Schena, Lorenzo; Rabault, Jean; Kuhnle, Alexander; Mendez, Miguel A. (CoRR, 2022)

Direct shape optimization through deep reinforcement learning. - Viquerat, Jonathan; Rabault, Jean; Kuhnle, Alexander; Ghraieb, Hassan; Larcher, Aurélien; Hachem, Elie (J. Comput. Phys., 2021)

Demonstration-Efficient Inverse Reinforcement Learning in Procedurally Generated Environments. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (2021)

Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (2021)

Reinforcement Learning for Information Retrieval. - Kuhnle, Alexander; Aroca-Ouellette, Miguel; Basu, Anindya; Sensoy, Murat; Reid, John; Zhang, Dell (2021)

Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (CoRR, 2021)

Evaluating visually grounded language capabilities using microworlds. - Kuhnle, Alexander (2020)

Process Discovery for Structured Program Synthesis. - Zhang, Dell; Kuhnle, Alexander; Richardson, Julian; Sensoy, Murat (CoRR, 2020)

DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (CoRR, 2020)

Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (CoRR, 2020)

Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games. - Sestini, Alessandro; Kuhnle, Alexander; Bagdanov, Andrew D. (CoRR, 2020)

The Meaning of "Most" for Visual Question Answering Models. - Kuhnle, Alexander; Copestake, Ann A. (2019)

What is needed for simple spatial language capabilities in VQA? - Kuhnle, Alexander; Copestake, Ann A. (2019)

A review on Deep Reinforcement Learning for Fluid Mechanics. - Garnier, Paul; Viquerat, Jonathan; Rabault, Jean; Larcher, Aurélien; Kuhnle, Alexander; Hachem, Elie (CoRR, 2019)

What is needed for simple spatial language capabilities in VQA? - Kuhnle, Alexander; Copestake, Ann A. (CoRR, 2019)

Direct shape optimization through deep reinforcement learning. - Viquerat, Jonathan; Rabault, Jean; Kuhnle, Alexander; Ghraieb, Hassan; Hachem, Elie (CoRR, 2019)

Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity. - Xie, Huiyuan; Sherborne, Tom; Kuhnle, Alexander; Copestake, Ann A. (CoRR, 2019)

How Clever Is the FiLM Model, and How Clever Can it Be? - Kuhnle, Alexander; Xie, Huiyuan; Copestake, Ann A. (2018)

LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations. - Schaarschmidt, Michael; Kuhnle, Alexander; Ellis, Ben; Fricke, Kai; Gessert, Felix; Yoneki, Eiko (CoRR, 2018)

How clever is the FiLM model, and how clever can it be? - Kuhnle, Alexander; Xie, Huiyuan; Copestake, Ann A. (CoRR, 2018)

The meaning of "most" for visual question answering models. - Kuhnle, Alexander; Copestake, Ann A. (CoRR, 2018)

ShapeWorld - A new test methodology for multimodal language understanding. - Kuhnle, Alexander; Copestake, Ann A. (CoRR, 2017)

Deep learning evaluation using deep linguistic processing. - Kuhnle, Alexander; Copestake, Ann A. (CoRR, 2017)

A Proposition-Based Abstractive Summariser. - Fang, Yimai; Zhu, Haoyue; Muszynska, Ewa; Kuhnle, Alexander; Teufel, Simone (2016)

Resources for building applications with Dependency Minimal Recursion Semantics. - Copestake, Ann A.; Emerson, Guy; Goodman, Michael Wayne; Horvat, Matic; Kuhnle, Alexander; Muszynska, Ewa (2016)