SWORD: An Efficient Adaptation of Multi-Agent Policy with Sparse and Low Rank Decomposition for Heterogeneous Satellite Cluster

This work investigates the adaptability of multi-agent policies to task changes in heterogeneous satellite clusters performing Earth observation missions. Since mission objectives and task requirements may vary across different mission scenarios, a policy trained for an initial task may experience a performance degradation when transferred to a new task, thereby requiring adaptation or fine-tuning. From a machine learning perspective, this problem can be formulated as a domain or task shift. Initially, the policy is trained using an on-policy multi-agent reinforcement learning (MARL) framework and subsequently transferred to a new task. To address the resulting performance degradation, we propose a policy adaptation approach based on Sparse and loW-Rank Decomposition (SWORD), which enables efficient fine-tuning of the previously learned policy by updating a reduced set of parameters. We first provide a theoretical analysis of the applicability of parameter-efficient fine-tuning within the MARL framework and subsequently validate the approach through experimental evaluation. The results demonstrate that SWORD's performance comparable to full-parameter fine-tuning while substantially reducing the number of trainable parameters. Furthermore, SWORD achieves improved performance compared with existing parameter-efficient fine-tuning baselines, demonstrating its potential for efficient policy adaptation in heterogeneous satellite cluster missions.