  This paper addresses the problem of decentralized spectrum sharing in
vehicle-to-everything (V2X) communication networks. The aim is to provide
resource-efficient coexistence of vehicle-to-infrastructure(V2I) and
vehicle-to-vehicle(V2V) links. A recent work on the topic proposes a
multi-agent reinforcement learning (MARL) approach based on deep Q-learning,
which leverages a fingerprint-based deep Q-network (DQN) architecture. This
work considers an extension of this framework by combining Double Q-learning
(via Double DQN) and transfer learning. The motivation behind is that Double
Q-learning can alleviate the problem of overestimation of the action values
present in conventional Q-learning, while transfer learning can leverage
knowledge acquired by an expert model to accelerate learning in the MARL
setting. The proposed algorithm is evaluated in a realistic V2X setting, with
synthetic data generated based on a geometry-based propagation model that
incorporates location-specific geographical descriptors of the simulated
environment(outlines of buildings, foliage, and vehicles). The advantages of
the proposed approach are demonstrated via numerical simulations.
