## 1 Introduction  
To address severe climate challenges, many countries have proposed the goal of \"net zero emissions\" strategies [1]. As an important means of local integration and local consumption of distributed renewable energy, the microgrid (MG) is of great significance for reducing carbon emissions. There have been many studies on the optimal scheduling of low-carbon MGs. Ref. [2] proposed a risk-based multi-objective energy exchange optimization for the networked MGs considering renewable power generation uncertainties. Refs. [3, 4] proposed management methods and operation strategies of integrated energy storage systems and demand-response, respectively, for the uncertainty of renewable energy. However, with the introduction of social funds in the MG construction, the traditional centralized optimal scheduling method cannot reflect the interests of different agents, and the multi-agent method provides a solution to the multi-agent management problem. In [5], power suppliers and consumers in the MG were modeled as autonomous agents, maximizing their own benefits through local decisions of each agent. [PERSON]. [6, 7] proposed a scheduling model based on a multi-agent game and reinforcement learning for an integrated energy microgrid. With the development of distributed generation, multiple MGs can operate under the same distribution network, forming a multi-microgrid (MMG) system. Ref. [8] proposed a cloud edge computing method for economic dispatch of active distribution network agents with MMG agents. Ref. [9] presents a stochastic programming model for the planning of MMGs in distribution networks. The decision-making process for a MMG is a partially observable Markov game. Although the multi-agent method can reflect the respective interests of different agents, there are still the following problems that need to be solved in the actual process: (1) The traditional game theory model is limited to a simple environment with complete information [10], making it difficult to solve the observable multi-agent decision-making problem this way. (2) The training process of multiple agents with partially observable information is non-stationary, and it is difficult to reach a stable equilibrium point. The authors of [11] used the alternating direction method of multipliers (ADMM) to solve the optimization problem of a multi-agent microgrid. The algorithm requires all parameters in advance, and it needs to obtain accurate renewable energy output, real-time prices. and other information. The authors of [3; 5; 6; 7] simulated the game process of MMG agents based on a Q-learning algorithm and found the best strategy, but Q-learning cannot handle high-dimensional continuous information, and its application scope is limited. The authors of [9; 12] adopted a heuristic algorithm but still needs an accurate model. Ref. [13] adopted a data-driven Monte Carlo method to make day-ahead decisions on retail electricity prices at the point of common coupling (PCC) of MMG, which does not require an explicit mathematical model but cannot be applied to real-time decision-making problems. With the development of artificial intelligence technology, advanced deep reinforcement learning provides a new way for the artificial intelligence driving of MMG agents. The authors of [14] applied an asynchronous advantage actor-critic (A3C) algorithm to MMG energy management. Unlike the above algorithm, it can be applied to continuous multi-dimensional action and state space. A novel real-time energy management strategy for an autonomous multi-energy management system based on the deep deterministic policy gradient (DDPG) algorithm was proposed in [15], which can learn from past experiences. Although the DDPG algorithm has excellent performance in solving uncertain problems [16], it cannot guarantee stability in the multi-agent scenario [17].