  This letter presents a deep reinforcement learning (DRL) approach for
transmission design to optimize the energy efficiency in vehicle-to-vehicle
(V2V) communication links. Considering the dynamic environment of vehicular
communications, the optimization problem is non-convex and mathematically
difficult to solve. Hence, we propose scenario identification-based double and
Dueling deep Q-Network (SI-D3QN), a DRL algorithm integrating both double deep
Q-Network and Dueling deep Q-Network, for the joint design of modulation and
coding scheme (MCS) selection and power control. To be more specific, we employ
SI techique to enhance link performance and assit the D3QN agent in refining
its decision-making processes. The experiment results demonstrate that, across
various optimization tasks, our proposed SI-D3QN agent outperforms the
benchmark algorithms in terms of the valid actions and link performance
metrics. Particularly, while ensuring significant improvement in energy
efficiency, the agent facilitates a 29.6% enhancement in the link throughput
under the same energy consumption.
