Investigating how observation history, action history, history length, and network structure affect recurrent Twin Delayed Deep Deterministic Policy Gradient (TD3) agents shows that action history is particularly important when observed responses depend on previous actions, and that processing past and current action-o...
Saki Omi, Hyo-Sang Shin, Namhoon Cho et al.· 4 citations
This work identifies a structural failure mode of this method class on terminal-navigation tasks: reaching a precise goal configuration while satisfying safety constraints that tighten along the final approach, and empirically demonstrates that adding a dense per-step success signal via an auxiliary value critic improv...
Aman Arora, Ricard M. Castan, Matteo El-Hariry et al.· 0 citations
A modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground that models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances.
Matteo El Hariry, Pedro U. Lima, Andrej Orsula et al.· 0 citations
HYPER-GNC, a multi-task reinforcement learning framework in which a hypernetwork maps physics-informed task embeddings to the weights of a shared actor-critic policy, enabling a single compact controller to master four distinct GNC tasks: velocity tracking, docking, inspection, and navigation with obstacle avoidance.
Ricard M. Castan, Aman Arora, Antoine Richard et al.· arXiv.org· 1 citation
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