Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments

• URL: http://arxiv.org/abs/2602.06088v1
• Date: Thu, 05 Feb 2026 04:57:58 GMT
• Title: Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments
• Abstract summary: We introduce a Transformer-based Reinforcement Learning framework for autonomous orbital collision avoidance.<n>A central contribution of this work is the use of transformer-based Partially Observable Markov Decision Process architecture.<n>This integration provides a foundation for training collision avoidance agents that can operate more reliably under imperfect monitoring environments.
• Score: 0.3093890460224435
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce a Transformer-based Reinforcement Learning framework for autonomous orbital collision avoidance that explicitly models the effects of partial observability and imperfect monitoring in space operations. The framework combines a configurable encounter simulator, a distance-dependent observation model, and a sequential state estimator to represent uncertainty in relative motion. A central contribution of this work is the use of transformer-based Partially Observable Markov Decision Process (POMDP) architecture, which leverage long-range temporal attention to interpret noisy and intermittent observations more effectively than traditional architectures. This integration provides a foundation for training collision avoidance agents that can operate more reliably under imperfect monitoring environments.

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