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)
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%.
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 presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused coordinating the play of two robots. Moreover, we are working on stabilizing the gait by adding additional sensor information. An ongoing work is the optimization of the control strategy by balancing between impedance and position control. By minimizing the jerk, gait and overall gameplay should improve significantly.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused on stabilizing the gait. Moreover, we would like to overcome the constraints of a ZMP-algorithm that has a horizontal footplate as precondition for the simplification of the equations. In addition we would like to switch between impedance and position control with a fuzzy-like algorithm that might help to minimize jerks when Sweaty’s feet touch the ground.
Due to the Covid-19 pandemic, the RoboCup WorldCup 2021 was held completely remotely. For this competition the Webots simulator (https://cyberbotics.com/) was used, so all teams needed to transfer their robot to the simulation. This paper describes our experiences during this process as well as a genetic learning approach to improve our walk engine to allow a more stable and faster movement in the simulation. Therefore we used a docker setup to scale easily. The resulting movement was one of the outstanding features that finally led to the championship title.