2 citations · 4 across the 4 of their papers we have counts for
5 papers
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Zhehua Zhou, Ozgur S. Oguz, Yi Ren +2
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing saf…
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
Ingmar Schubert, Ozgur S. Oguz, Marc Toussaint
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward…
Visualization of Nonlinear Programming for Robot Motion Planning
David Hägele, Moataz Abdelaal, Ozgur S. Oguz +2
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem s…
Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation
Joaquim Ortiz-Haro, Valentin N. Hartmann, Ozgur S. Oguz +1
Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a p…
Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems
Zhehua Zhou, Ozgur S. Oguz, Marion Leibold +1
For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning…