Real-time video communication is profoundly changing people's lives, especially in today's pandemic situation. However, packet loss during video transmission degrades reconstructed video quality, thus impairing users' Quality of Experience (QoE). Forward Error Correction (FEC) techniques are commonly employed in today's audio and video conferencing applications, such as Skype and Zoom, to mitigate the impact of packet loss. FEC helps recover the lost packets during transmissions at the receiver side, but the additional bandwidth consumption is also a concern. Since network conditions are highly dynamic, it is not trivial for FEC to maintain video quality with a fixed bandwidth overhead. In this paper, we propose RL-AFEC, an adaptive FEC scheme based on Reinforcement Learning (RL) to improve reconstructed video quality with an aim to mitigate bandwidth consumption for different network conditions. RL-AFEC learns to select a proper redundancy rate for each video frame, and then adds redundant packets based on the frame-level Reed-Solomon (RS) code. We also implement a novel packet-level Video Quality Assessment (VQA) method based on Video Multimethod Assessment Fusion (VMAF), which leverages Supervised Learning (SL) to generate video quality scores in real time by only extracting information from the packet stream without the need of visual contents. Extensive evaluations demonstrate the superiority of our scheme over other baseline FEC methods.