Abstract: The existing task scheduling algorithms based on deep reinforcement learning(DRL) in cloud data center have the following problems.High training cost caused by low effective experience utilization,learning oscillation caused by variable and high dimension of state space,and slow convergence speed caused by fixed step size of policy update.In order to solve the above pro-blems,this paper constructs a parallel task scheduling framework based on the cloud data center scenario,and studies the cloud task scheduling problem with the goal of delay,energy consumption and load balancing.Based on DRL algorithm A2C,this paper proposes a task scheduling algorithm for cloud data center based on adaptive state optimization and dynamic step size A2C(AODS-A2C).Firstly,the admission control and priority strategy are used to filter and sort the queue tasks to improve the utilization of effective experience.Secondly,the dynamic high-dimensional state is quickly optimized in an adaptive way to maintain a relatively stable state space and avoid the oscillation problem in the training process.Finally,JS(Jensen Shannon) divergence is used to measure the probability distribution difference between the old and new strategies,and the learning step size of Actor network and Critic network is dynamically adjusted according to this difference,so as to quickly adjust to the best value for the current learning state and improve the convergence speed of the algorithm.The simulation results show that the proposed AOS-A2C algorithm has the characteristics of fast convergence speed and high robustness.Compared with other comparison algorithms,the delay is reduced by 1.2% to 34.4%,and the energy consumption is reduced by 1.6% to 57.2%,and it can achieve good load ba-lancing.

Key words: Cloud computing, Task scheduling, Deep reinforcement learning, State optimization, JS divergence