Refine
Year of publication
Document Type
- Conference Proceeding (52)
- Contribution to a Periodical (4)
- Report (2)
- Article (unreviewed) (1)
Conference Type
- Konferenzartikel (21)
- Sonstiges (19)
- Konferenz-Abstract (12)
Is part of the Bibliography
- yes (59)
Keywords
- RoboCup (31)
- Roboter (6)
- Humanoider Roboter (2)
- Machine Learning (2)
- Agentbasierter Transport (1)
- Deep Learning (1)
- Deep Reinforcement Learning (1)
- Entscheidungstheorie (1)
- Fußball (1)
- Humanoid Robots (1)
- InceptionTime (1)
- Informatik (1)
- Kosten (1)
- Künstliche Intelligenz (1)
- Lastkraftwagen (1)
- Logistik (1)
- Logistikunternehmen (1)
- Optimierung (1)
- ResNet (1)
- Road-Quality Prediction (1)
- Robotic Soccer (1)
- Sensortechniik (1)
- Time-series Classification (1)
- Transport (1)
- Transportproblem (1)
- agent-based systems (1)
- agent-based transport optimization (1)
- curriculum learning (1)
- deep reinforcement learning (1)
- genetic algorithms (1)
- humanoid robot walking (1)
- machine learning (1)
Institute
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (35)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (21)
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (13)
- IMLA - Institute for Machine Learning and Analytics (5)
- Fakultät Medien und Informationswesen (M+I) (bis 21.04.2021) (4)
- Fakultät Wirtschaft (W) (3)
- INES - Institut für nachhaltige Energiesysteme (2)
- Zentrale Einrichtungen (2)
Open Access
- Open Access (52)
- Bronze (34)
- Closed Access (5)
- Closed (2)
- Grün (2)
- Diamond (1)
In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs. In Curriculum Learning, the Machine Learning algorithm is not randomly presented with examples, but in a meaningful order of increasing difficulty. This has been used in many application areas to further improve the results of learning systems or to reduce their learning time. Such approaches range from learning plans created manually by domain experts to those created automatically. The automated creation of learning plans is one of the biggest challenges.In this work, we investigate an approach in which a trainer learns in parallel and analogously to the student to automatically create a learning plan for the student during this Double Deep Reinforcement Learning (DDRL). Three Reward functions, Friendly, Adversarial, and Dynamic based on the learner’s reward are compared. The domain for evaluation is kicking with variable distance, direction and relative ball position in the SimSpark simulated soccer environment.As a result, Statistic Curriculum Learning (SCL) performs better than a random curriculum with respect to training time and result quality. DDRL reaches a comparable quality as the baseline and outperforms it significantly in shorter trainings in the distance-direction subdomain reducing the number of required training cycles by almost 50%.
This paper describes the magmaOffenburg 3D simulation team trying to qualify for RoboCup 2011. While last year’s TDP focused on the tool set created for 3D simulation in this year we describe the further improvement in this tools as well as some new features we implemented focusing on heterogeneous robot models which seem to be used in RoboCup 2012.
An additional tool was written to simply generate situation-dependent strategies. Furthermore some tools, described last year, are now integrated in one single GUI to easy things up.
After having described many different aspects of our team software in previous years, in this paper we take the freedom to describe the magmaChallenge framework provided by the magmaOffenburg team. The framework is used as a benchmark tool to run different challenges like the running challenge in 2014 or the kick accuracy challenge in 2015. This description should serve as a documentation to simplify the maintenance by the community and to add new benchmarks in the future.
Team description papers of magmaOffenburg are incremental in the sense that each year we address a different topic of our team and the tools around our team. In this year’s team description paper we focus on the architecture of the software. It is a main factor for being able to keep the code maintainable even after 15 years of development. We also describe how we make sure that the code follows this architecture.
This paper describes the magmaOffenburg 3D simulation team trying to qualify for RoboCup 2012. While last year’s TDP focused on the tool set created for 3D simulation and the support for heterogeneous robot models, this year we focus on the different ways how robot behavior can be defined in the magmaOffenburg framework and how those behaviors can be improved by learning.
This paper describes the new Sweaty humanoid adult size robot trying to qualify for the RoboCup 2014 adult size humanoid competition. The robot is built from scratch to eventually allow it to run. One characteristic is that to prevent the motors from overheating, water evaporation is used for cooling. The robot is literally sweating which has given it its name. Another characteristic is, that the motors are not directly connected to the frame but by means of beams. This allows a variable transmission ratio depending on the angle.
Die Einhaltung der innerhalb der Designphase festgelegten Architektur eines Softwareprojektes muss w ̈ahrend der Entwicklungsphase sichergestellt werden. Dieses Papier beschreibt eine Erweiterung des Eclipse-Plugins JDepend4Eclipse, die die Verwaltung von Regels ̈atzen erlaubt und die Pr ̈ufung auf in einem Projekt vorhandene, unerlaubte Abh ̈angigkeiten auf Knopfdruck innerhalb der Entwicklungsumgebung vornimmt. Die Erweiterung des Plugins wird bereits erfolgreich in internen Projekten der Hochschule Offenburg eingesetzt und soll demn ̈achst ̈offentlich verf ̈ugbar sein.