Enhancing Autonomous Space Exploration with Distributed Case-Based Reasoning and Learning (DCBRL) in Multi-Agent Systems

Space exploration robots must operate autonomously due to the challenges posed by communication delays and power constraints, especially in dynamic and  unpredictable extraterrestrial environments. Decentralized Multi-Agent Reinforcement Learning (MARL) offers a potential solution by enabling agents to operate without the need for continuous communication with a central controller, thus alleviating communication delay issues. However, traditional MARL approaches are not inherently optimized for power efficiency, and suffer from non-stationarity issues, which can destabilize the learning process. To address these challenges, we propose a preliminary version of an innovative solution that combines distributed CaseBased Reasoning (CBR) and MARL to form a Distributed Case-Based Reasoning and Learning (DCBRL) implemented in a decentralized way. DCBRL addresses the challenges of nonstationarity and dynamic environmental changes through a trust-based mechanism that allows
agents to adapt quickly and share successful strategies. By leveraging QCBRL principles, the proposed system enables autonomous agents, such as planetary rovers or drones, to cooperate efficiently in unpredictable extraterrestrial environments, ensuring mission success despite communication delays.