3 papers
cs.RO2025
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo +1
Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discr…
cs.LG2024
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp +2
Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slo…
cs.LG2024
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
Mirco Theile, Hongpeng Cao, Marco Caccamo +1
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance. However, ensuring that the deep RL policy and…