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5G vehicle-to-everything (V2X) connectivity is crucial to enable future complex vehicular networking environment for enabling intelligent transportation systems (ITS). But, for mission critical applications like safety applications, unreliable vehicle-to-vehicle (V2V) connections and heavy signaling overheads in centralized resource distribution methods are becoming key obstacles. This work discusses the popular optimization issue of the selection of transmission mode and the allocation of resources blocks for 5G-V2X communication scenario. The stated problem is conceived as a Markov decision-making mechanism, and a Decentralized Deep reinforcement Learning (DRL) algorithm is presented to optimize the aggregate potential in terms of channel capacity of vehicle-to-infrastructure users while fulfilling the latency and reliability constraints of V2V communication link sets. In addition, considering training limitation …