  Deep reinforcement learning (DRL) has been increasingly employed to handle
the dynamic and complex resource management in network slicing. The deployment
of DRL policies in real networks, however, is complicated by heterogeneous cell
conditions. In this paper, we propose a novel transfer learning (TL) aided
multi-agent deep reinforcement learning (MADRL) approach with inter-agent
similarity analysis for inter-cell inter-slice resource partitioning. First, we
design a coordinated MADRL method with information sharing to intelligently
partition resource to slices and manage inter-cell interference. Second, we
propose an integrated TL method to transfer the learned DRL policies among
different local agents for accelerating the policy deployment. The method is
composed of a new domain and task similarity measurement approach and a new
knowledge transfer approach, which resolves the problem of from whom to
transfer and how to transfer. We evaluated the proposed solution with extensive
simulations in a system-level simulator and show that our approach outperforms
the state-of-the-art solutions in terms of performance, convergence speed and
sample efficiency. Moreover, by applying TL, we achieve an additional gain over
27% higher than the coordinate MADRL approach without TL.
