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This paper proposes a deep reinforcement learning (DRL)‐based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the signal‐interference‐plus‐noise ratio‐based vehicle‐to‐vehicle communication is incorporated into the DRL training environment to reproduce the realistic communication and time–space varying information flow topologies (IFTs). A dynamic information fusion mechanism is designed to smooth the high‐jerk control signal caused by the dynamic IFTs. Based on that, each CAV controlled by the DRL‐based agent was developed to receive the real‐time downstream CAVs’ state information and take longitudinal actions to achieve the equilibrium consensus in the multi‐agent system. Simulated experiments are conducted to tune the communication adjustment mechanism and further validate …