[Objective] To address the strong uncertainty in renewable energy output and load demand, complex operational constraints, and continuous decision variables in the cooperative scheduling of multi-microgrid systems, this paper proposes an economic optimization method based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. [Methods] Firstly, a three-microgrid cluster model was constructed, incorporating wind power, photovoltaic generation, micro-turbines, energy storage systems, EV charging loads, and main grid interactions. Its economic optimal dispatch process was modeled as a Markov decision process. The state space encompassed load demand, renewable generation, time-of-use electricity prices, and battery state of charge, while the action space comprised energy storage charge/discharge power, micro-turbine output, power exchange between microgrids, and interaction power with the main grid. Next, a reward function integrating normalized operating costs, environmental costs, and constraint penalty terms was designed to guide the agent in learning economical and feasible scheduling strategies. Finally, building on the original TD3 algorithm, a dual experience pool (DEP) mechanism and an adaptive exponentially decaying Gaussian exploration noise (AED-GEN) mechanism were introduced. The DEP mechanism classified experience samples into feasible solution samples and boundary exploration samples, improving sample utilization efficiency and enhancing the learning capability for constraint boundaries. Meanwhile, the AED-GEN mechanism balanced exploration during early training and convergence stability in later stages. [Results] Case studies were conducted on a three-microgrid cluster. The results demonstrated that the improved TD3 algorithm outperformed the deep deterministic policy gradient (DDPG), original TD3, and SAC algorithms in terms of convergence stability, operational economy, and scheduling security. The standard deviation of reward fluctuation in the last 100 training episodes for the improved TD3 was 1.51, representing reductions of 35.74%, 45.09%, and 54.24% compared to SAC, original TD3, and DDPG, respectively. Its total daily operating cost was 4,186.94 yuan, which was 1.38%, 6.31%, and 8.71% lower than that of SAC, original TD3, and DDPG, respectively. Meanwhile, the action limit violation rate dropped rapidly during training and gradually approached zero in the later stages. Further comparisons of the improved mechanisms verified that both the adaptive exploration noise mechanism and the dual experience pool mechanism enhanced algorithm performance. [Conclusion] By satisfying the physical operational constraints, the proposed improved TD3 method leverages the mutual support capability among multiple entities within the microgrid cluster, thereby enhancing the economic efficiency and security of the system’s collaborative scheduling.