These authors present a few critical features for ACMPTC to enhance applications require high bandwidth, ultra-low latency, and its performance--mainly choosing paths with low latency and consistent quality of service (QoS) to deliver seamless, immersive packet loss. It brings a DRL-based agent that can adapt its experiences [2]. Traditional network protocols like the decision to real-time network states and compute dynamic, Transmission Control Protocol (TCP) often struggle to meet optimal choices. This feedback loop, on the other hand, these stringent demands, especially in highly dynamic and allows for real-time path selection and resource allocation that diverse network environments due to single path transmission, enables continuous optimization to provide a smooth AR/VR inadequate for high-bandwidth, low-latency requirement, high experience even with varying network conditions. It confirms latency sensitivity, etc. [3]. These limitations make TCP less that the system operates correctly and provides a way to update effective for dynamic, heterogeneous network environments such a network when there is variation in traffic levels by and the demanding performance needs of modern applications adjusting it effectively.

Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications. Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently. MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency. Although MPTCP has shown better performance than TCP in many applications, it has its own challenges. The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly. Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows. This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine Learning) approaches. This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications. This paper also provides details on the simulation of MPTCP and its implementations in real environments.

The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL. The existing transmission control protocol (TCP)-based data networking schemes for DEL are application-agnostic and fail to deliver adjustments according to application layer requirements. As a result, they introduce massive excess time and undesired issues such as unfairness and stragglers. Other prior mitigation solutions have significant limitations as they balance data flow rates from workers across paths but often incur imbalanced backlogs when the paths exhibit variance, causing stragglers. To facilitate a more productive DEL, we develop a hybrid multipath TCP (MPTCP) by combining model-based and deep reinforcement learning (DRL) based MPTCP for DEL that strives to realize quicker iteration of DEL and better fairness (by ameliorating stragglers). Hybrid MPTCP essentially integrates two radical TCP developments: i) successful existing model-based MPTCP control strategies and ii) advanced emerging DRL-based techniques, and introduces a novel hybrid MPTCP data transport for easing the communication of the Agg-Avg process. Extensive emulation results demonstrate that the proposed hybrid MPTCP can overcome excess time consumption and ameliorate the application layer unfairness of DEL effectively without injecting additional inconstancy and stragglers.