Dynamic voltage and frequency scaling (DVFS) is an effective approach to balance the performance and power consumption of the CPU-GPU-based video rendering applications. Operating System (OS) based DVFS governors are general in nature and thus lack of regulation effectiveness for workload with different characteristics. Reinforcement learning (RL) enhances power management ability for application-specific DVFS, whereas the regulation robustness is limited due to partial observability of complex SoCs. To mitigate this issue, in this paper, a deep recurrent Q-Network (DRQN) based DVFS governor for rendering applications is proposed where a recurrent neural network (RNN) exploiting historical information is embedded in a Deep Q-Network (DQN). Evaluated on the Nvidia Jetson NX platform with various settings, the proposed DRQN policy achieves an up to ×2.11 improvement in Valid Performance Per Watt (VPPW) compared with Linux default governors and shows superior regulation stability compared with other RL-based state-of-the-art.