Multi-path transmission control protocol data packet scheduling method based on deep reinforcement learning

The invention discloses a multi-path transmission control protocol data packet scheduling method based on deep reinforcement learning. In a multi-path transmission control protocol (MPTCP), a data packet scheduling process is converted into a Markov decision process by setting a periodic scheduling mechanism, a data packet scheduling strategy of the MPTCP is represented by a neural network through deep reinforcement learning, and an optimal data packet scheduling strategy under various network environments is learned. The problem that heuristic MPTCP data packet scheduling cannot adapt to complex and diverse dynamic network environments, and consequently MPTCP performance is reduced is fundamentally solved. According to the method, an Actor-Critic reinforcement learning framework is used, a deep reinforcement learning algorithm based on strategy gradient is adopted, and modeling and learning are directly carried out on an MPTCP data packet scheduling strategy, so that strategy optimization efficiency is improved, and training of an MPTCP data packet scheduling strategy neural network is accelerated.