mlsanityData to 14 Oct 2026

Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint

arXiv 2511.13746 ↗First posted 13 Nov 2025eess.SY cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact representation of the attitude constraint zone. The reward function is formulated to achieve the control objective while enforcing the attitude constraint. A curriculum learning approach is used for the agent training. Simulation results demonstrate the effectiveness of the proposed DRL-based method for spacecraft pointing-constrained attitude control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 10 references that are in the map. First posted on arXiv 13 Nov 2025.

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1 of 10 references.

References · 1

  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 483

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  1. 2026Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint
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  4. 2025LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller
  5. 2026Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System ↗
  6. 2025Imitation Learning for Satellite Attitude Control under Unknown Perturbations
  7. 2025Intelligent Control of Spacecraft Reaction Wheel Attitude Using Deep Reinforcement Learning
  8. 2025Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)