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Adaptive bitrate (ABR) algorithms are used to adapt the video bitrate basedon the network conditions to improve the overall video quality of experience(QoE). Recently, reinforcement learning (RL) and asynchronous advantageactor-critic (A3C) methods have been used to generate adaptive bit ratealgorithms and they have been shown to improve the overall QoE as compared tofixed rule ABR algorithms. However, a common issue in the A3C methods is thelag between behaviour policy and target policy. As a result, the behaviour andthe target policies are no longer synchronized which results in suboptimalupdates. In this work, we present ALISA: An Actor-Learner Architecture withImportance Sampling for efficient learning in ABR algorithms. ALISAincorporates importance sampling weights to give more weightage to relevantexperience to address the lag issues with the existing A3C methods. We presentthe design and implementation of ALISA, and compare its performance tostate-of-the-art video rate adaptation algorithms including vanilla A3Cimplemented in the Pensieve framework and other fixed-rule schedulers like BB,BOLA, and RB. Our results show that ALISA improves average QoE by up to 25%-48%higher average QoE than Pensieve, and even more when compared to fixed-ruleschedulers.

Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services