  Satellite networks provide communication services to global users with an
uneven geographical distribution. In densely populated regions, Inter-satellite
links (ISLs) often experience congestion, blocking traffic from other links and
leading to low link utilization and throughput. In such cases, delay-tolerant
traffic can be withheld by moving satellites and carried to navigate congested
areas, thereby mitigating link congestion in densely populated regions. Through
rational store-and-forward decision-making, link utilization and throughput can
be improved. Building on this foundation, this letter centers its focus on
learning-based decision-making for satellite traffic. First, a link load
prediction method based on topology isomorphism is proposed. Then, a Markov
decision process (MDP) is formulated to model store-and-forward
decision-making. To generate store-and-forward policies, we propose
reinforcement learning algorithms based on value iteration and Q-Learning.
Simulation results demonstrate that the proposed method improves throughput and
link utilization while consuming less than 20$\%$ of the time required by
constraint-based routing.
