Eagle takes advantage of expert knowledge from an existing algorithm, and uses deep reinforcement learning (DRL) to train a generalized model with the hope of ...

Abstract—Traditional congestion control algorithms were de- signed with a hardwired heuristic mapping between packet- level events and predefined control ...

Eagle, a new congestion control algorithm to refine existing heuristics, takes advantage of expert knowledge from an existing algorithm, and uses deep ...

A new direction to deal with the dynamic changes in the network is to let RL solutions determine the congestion control actions instead of relying on ...

Eagle: Refining Congestion Control by Learning from the Experts. S. Emara, B. Li, and Y. Chen. INFOCOM, page 676-685. IEEE, (2020 ). 1. 1. Meta data.

Mar 28, 2024 · Chen, “Eagle: Refining congestion control by learning from the experts,” in IEEE INFOCOM 2020 - IEEE Conference on Computer Communications ...

Eagle: Refining Congestion Control by Learning from the Experts. S Emara, B Li, Y Chen. Proceedings of IEEE International Conference on Computer Communications ...

Chen, “Eagle: Refining Congestion. Control by Learning from the Experts,” Proc. 39th IEEE Conf. Computer Commun. (INFOCOM), 2020, pp. 676–85. [14] Z. Xu et ...

Emara, B. Li, and Y. Chen, “Eagle: Refining congestion control by learning from the experts,” in IEEE INFOCOM 2020-IEEE Conference.

Sep 23, 2023 · Eagle: Refining congestion control by learning from the experts. In ... Reinforcement learning based congestion control in a real environment.