  In recent years, with the large-scale deployment of space spacecraft entities
and the increase of satellite onboard capabilities, delay/disruption tolerant
network (DTN) emerged as a more robust communication protocol than TCP/IP in
the case of excessive network dynamics. DTN node buffer management is still an
active area of research, as the current implementation of the DTN core protocol
still relies on the assumption that there is always enough memory available in
different network nodes to store and forward bundles. In addition, the
classical queuing theory does not apply to the dynamic management of DTN node
buffers. Therefore, this paper proposes a centralized approach to automatically
manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation
scenarios based on the advanced reinforcement learning (RL) strategy advantage
actor-critic (A2C). The method aims to explore training a geosynchronous earth
orbit intelligent agent to manage all DTN nodes in an LEO satellite
constellation scenario. The goal of the A2C agent is to maximize delivery
success rate and minimize network resource consumption cost while considering
node memory utilization. The intelligent agent can dynamically adjust the radio
data rate and perform drop operations based on bundle priority. In order to
measure the effectiveness of applying A2C technology to DTN node management
issues in LEO satellite constellation scenarios, this paper compares the
trained intelligent agent strategy with the other two non-RL policies,
including random and standard policies. Experiments show that the A2C strategy
balances delivery success rate and cost, and provides the highest reward and
the lowest node memory utilization.
