  Platooning is considered one of the most representative 5G use cases. Due to
the small spacing within the platoon, the platoon needs more reliable
transmission to guarantee driving safety while improving fuel and driving
efficiency. However, efficient resource allocation between platoons has been a
challenge, especially considering that the channel and power selected by each
platoon will affect other platoons. Therefore, platoons need to coordinate with
each other to ensure the groupcast quality of each platoon. To solve these
challenges, we model the multi-platoon resource selection problem as Markov
games and then propose a distributed resource allocation algorithm based on
Value-Decomposition Networks. Our scheme utilizes the historical data of each
platoon for centralized training. In distributed execution, agents only need
their local observations to make decisions. At the same time, we decrease the
training burden by sharing the neural network parameters. Simulation results
show that the proposed algorithm has excellent convergence. Compared with
another multi-agent algorithm (MARL) and random algorithm, our proposed
solution can dramatically reduce the probability of platoon groupcast failure
and improve the quality of platoon groupcast.
