Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems

2025-01-10

AI Summary

This study investigates how observation-space design affects the training efficacy of reinforcement learning (RL) agents in spacecraft autonomous inspection tasks, with the goal of enhancing algorithmic interpretability and pedagogical utility. We propose a comparative experimental framework incorporating multiple sensor configurations and coordinate transformations across diverse reference frames to systematically assess the impact of sensor modality and reference-frame selection on policy learning. Results show that while sensors are not strictly necessary, their inclusion significantly improves policy optimality; conversely, the specific mathematical form of the reference frame has negligible influence, whereas consistency of the reference frame across states is critical for training stability and convergence quality. We introduce, for the first time, the design principle “reference-frame consistency outweighs specific choice” for RL environments—a finding that provides both theoretical grounding and practical guidance for developing lightweight, interpretable RL systems tailored to educational settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Reinforcement Learning
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent research using Reinforcement Learning (RL) to learn autonomous control for spacecraft operations has shown great success. However, a recent study showed their performance could be improved by changing the action space, i.e. control outputs, used in the learning environment. This has opened the door for finding more improvements through further changes to the environment. The work in this paper focuses on how changes to the environment's observation space can impact the training and performance of RL agents learning the spacecraft inspection task. The studies are split into two groups. The first looks at the impact of sensors that were designed to help agents learn the task. The second looks at the impact of reference frames, reorienting the agent to see the world from a different perspective. The results show the sensors are not necessary, but most of them help agents learn more optimal behavior, and that the reference frame does not have a large impact, but is best kept consistent.

Problem

• Reinforcement Learning
• Observation Space Design
• Educational Effectiveness

Innovation

• Reinforcement Learning
• Observation Space Design
• Spacecraft Maneuvering Problem