Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Kuhnle, Alexander"'
In this article we study the problem of training intelligent agents using Reinforcement Learning for the purpose of game development. Unlike systems built to replace human players and to achieve super-human performance, our agents aim to produce mean
Externí odkaz:
http://arxiv.org/abs/2104.10610
This research reports on the recent development of a black-box optimization method based on single-step deep reinforcement learning (DRL), and on its conceptual proximity to evolution strategy (ES) techniques. In the fashion of policy gradient (PG) m
Externí odkaz:
http://arxiv.org/abs/2104.06175
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. The use of these techniques as a game design tool for video game
Externí odkaz:
http://arxiv.org/abs/2012.03532
Deep Reinforcement Learning achieves very good results in domains where reward functions can be manually engineered. At the same time, there is growing interest within the community in using games based on Procedurally Content Generation (PCG) as ben
Externí odkaz:
http://arxiv.org/abs/2012.02527
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). Our aim is to understand whether recent advances in
Externí odkaz:
http://arxiv.org/abs/2012.01914
A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models so as to establish connections to th
Externí odkaz:
http://arxiv.org/abs/2008.05804
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). More precisely, the proximal policy optimization (PPO) method is used to control the
Externí odkaz:
http://arxiv.org/abs/2004.12417
Image captioning as a multimodal task has drawn much interest in recent years. However, evaluation for this task remains a challenging problem. Existing evaluation metrics focus on surface similarity between a candidate caption and a set of reference
Externí odkaz:
http://arxiv.org/abs/1912.08960
Autor:
Viquerat, Jonathan, Rabault, Jean, Kuhnle, Alexander, Ghraieb, Hassan, Larcher, Aurélien, Hachem, Elie
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements. Still, much remains to be explored before the capabilities of these methods are well understood. In t
Externí odkaz:
http://arxiv.org/abs/1908.09885
Autor:
Kuhnle, Alexander, Copestake, Ann
Visual question answering (VQA) comprises a variety of language capabilities. The diagnostic benchmark dataset CLEVR has fueled progress by helping to better assess and distinguish models in basic abilities like counting, comparing and spatial reason
Externí odkaz:
http://arxiv.org/abs/1908.06336