TRM-PL uses a 2.3M-parameter weight-shared recursive architecture to reduce median position errors to 0.027 km on single-revolution LEO and 0.31 km on multi-revolution LEO transfers via position-supervised refinement.
Wijayatunga, Roberto Armellin, Harry Holt, Laura Pirovano, and Aleksander A
2 Pith papers cite this work. Polarity classification is still indexing.
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Empirical comparison finds Mamba with PPO superior to LSTM/GRU and SAC variants for meta-RL tuning of ICCBF class-K functions in cooperative and adversarial spacecraft RPO simulations.
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Tiny Recursive Models for Solving the J2-Perturbed Lambert Problem
TRM-PL uses a 2.3M-parameter weight-shared recursive architecture to reduce median position errors to 0.027 km on single-revolution LEO and 0.31 km on multi-revolution LEO transfers via position-supervised refinement.
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Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations
Empirical comparison finds Mamba with PPO superior to LSTM/GRU and SAC variants for meta-RL tuning of ICCBF class-K functions in cooperative and adversarial spacecraft RPO simulations.