no code implementations • 28 Feb 2024 • Mahya Ramezani, Jose Luis Sanchez-Lopez
Our contributions include (1) a reinforcement learning framework for UAV trajectory planning that dynamically integrates multi-objective considerations, (2) an analysis of human perceptions towards gendered and anthropomorphized drones in SAR contexts, and (3) the application of similarity-based experience replay for enhanced learning efficiency in complex SAR scenarios.
Multi-Objective Reinforcement Learning reinforcement-learning +1
no code implementations • 21 Sep 2023 • Mahya Ramezani, M. Amin Alandihallaj, Jose Luis Sanchez-Lopez, Andreas Hein
This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO).
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 21 Feb 2023 • Mahya Ramezani, Hamed Habibi, Jose luis Sanchez Lopez, Holger Voos
In this paper, we tackle the problem of Unmanned Aerial (UA V) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm.