This is an easy TensorFlow implementation of Pensieve[1]. In detail, we trained Pensieve via PPO rather than A3C. It's a stable version, which has already prepared the training set and the test set, and you can run the repo easily: just type
python train.py
instead. Results will be evaluated on the test set (from HSDPA) every 300 epochs.
We reported the training curve of entropy weight beta, reward, and entropy respectively. Results were evaluated over the Oboe network traces.
Tips: the orange curve: pensieve-ppo; the blue curve: pensieve-a2c
Furthermore, we have also added the pretrained model in https://github.com/godka/Pensieve-PPO/tree/master/src/pretrain
The model improves 7.03% (0.924->0.989) on average QoE compared with the original Pensieve model [1].
Please feel free to let me know if you have any questions.
[1] Mao H, Netravali R, Alizadeh M. Neural adaptive video streaming with pensieve[C]//Proceedings of the Conference of the ACM Special Interest Group on Data Communication. ACM, 2017: 197-210.