mlsanityData to 7 Oct 2026

Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning

arXiv 1911.00489 ↗First posted 1 Nov 2019eess.SY cs.SYcited by 1

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Inverse Reinforcement Learning (RL) can be used to determine the behavior of Space Objects (SOs) by estimating the reward function that an SO is using for control. The approach discussed in this work can be used to analyze maneuvering of SOs from observational data. The inverse RL problem is solved using maximum causal entropy. This approach determines the optimal reward function that a SO is using while maneuvering with random disturbances by assuming that the observed trajectories are optimal with respect to the SO's own reward function. Lastly, this paper develops results for scenarios involving Low Earth Orbit (LEO) station-keeping and Geostationary Orbit (GEO) station-keeping.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 2 of its 29 references that are in the map. First posted on arXiv 1 Nov 2019.

Builds on

2 of 29 references.

References · 2

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173

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Cited by year

Citing papers per year: 2021: 1.

1 paper.

  1. 2021Advances in Trajectory Optimization for Space Vehicle Controlcited by 5

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