Apr 21, 2022 · The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training ...

Specifically, DeepCC leverages multi-objective optimization to offline generate a flexible agent that is able to learn the Pareto optimal or near-optimal ...

The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a ...

The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a ...

The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a ...

DeepCC: Bridging the Gap Between Congestion Control and Applications via Multiobjective Optimization. IEEE/ACM Trans. Netw. 30(5): 2274-2288 (2022); 2021. [i1].

Deepcc: Bridging the gap between congestion control and applications via multiobjective optimization. L Zhang, Y Cui, M Wang, K Zhu, Y Zhu, Y Jiang. IEEE/ACM ...

DeepCC: Bridging the Gap Between Congestion Control and Applications via Multiobjective Optimization · Author Picture Lei Zhang. Department of Computer Science ...

This work proposes a framework, ARC, for learning congestion control policies in a real environment based on asynchronous execution and demonstrates its ...

DeepCC: Bridging the Gap Between Congestion Control and Applications via Multiobjective Optimization · Lei ZhangYong CuiMowei WangKewei ZhuYibo ZhuYong Jiang.