Patrik Kolaric
Papers
1
Total Citations
2
H-Index
1
About
Patrik Kolaric is a researcher advancing the frontier of model-based reinforcement learning (MBRL) for complex, nonlinear control systems. His primary focus lies in developing algorithms that bridge trajectory optimization and local policy synthesis, enabling more stable and efficient learning in robotics and autonomous systems. In his seminal 2020 work, "Local Policy Optimization for Trajectory-Centric Reinforcement Learning," Kolaric introduced a method that simultaneously optimizes trajectories and local stabilizing policies, addressing a critical gap in MBRL where global policy optimization often struggles with high-dimensional, nonlinear dynamics. This approach has garnered attention for its potential to improve sample efficiency and safety in real-world applications, such as drone navigation and robotic manipulation. While his citation count is still growing, reflecting the early stage of his career, Kolaric’s work is recognized for its theoretical rigor and practical promise, contributing to the broader push toward more reliable, data-efficient learning in control. His research is particularly relevant for students and engineers seeking to integrate reinforcement learning with traditional control theory.
Research Focus
Key Achievements
Top Papers
- 1Local Policy Optimization for Trajectory-Centric Reinforcement Learning2 citations · 2020