no code implementations • 21 Apr 2021 • Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
To this end, we propose four different policy fusion methods for combining pre-trained policies.
no code implementations • 7 Dec 2020 • Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environments.
no code implementations • 4 Dec 2020 • Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games.
no code implementations • 3 Dec 2020 • Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL).
no code implementations • 13 Aug 2020 • Dell Zhang, Alexander Kuhnle, Julian Richardson, Murat Sensoy
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
1 code implementation • 26 Apr 2020 • Hongwei Tang, Jean Rabault, Alexander Kuhnle, Yan Wang, Tongguang Wang
This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL).
Fluid Dynamics
no code implementations • 19 Dec 2019 • Huiyuan Xie, Tom Sherborne, Alexander Kuhnle, Ann Copestake
Image captioning as a multimodal task has drawn much interest in recent years.
4 code implementations • 23 Aug 2019 • Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, Elie Hachem
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements.
Computational Engineering, Finance, and Science
no code implementations • 17 Aug 2019 • Alexander Kuhnle, Ann Copestake
Visual question answering (VQA) comprises a variety of language capabilities.
1 code implementation • 12 Aug 2019 • Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, Elie Hachem
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
4 code implementations • 25 Jun 2019 • Jean Rabault, Alexander Kuhnle
In the case of AFC trained with Computational Fluid Mechanics (CFD) data, it was found that the CFD part, rather than the training of the Artificial Neural Network, was the limiting factor for speed of execution.
Computational Physics
no code implementations • 31 Dec 2018 • Alexander Kuhnle, Ann Copestake
The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.
no code implementations • 9 Sep 2018 • Alexander Kuhnle, Huiyuan Xie, Ann Copestake
The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture.
4 code implementations • 23 Aug 2018 • Michael Schaarschmidt, Alexander Kuhnle, Ben Ellis, Kai Fricke, Felix Gessert, Eiko Yoneki
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
no code implementations • WS 2018 • Alexander Kuhnle, Ann Copestake
We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice.
3 code implementations • 14 Apr 2017 • Alexander Kuhnle, Ann Copestake
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities.