To address the uncertainty and scheduling complexity intro-duced by high-proportion renewable energy integration, this paper proposes a low-carbon cooperative dispatch strategy for hydro-wind-solar-storage systems based on an improved TD3 algorithm. A system operation model encompassing hydro-power, wind power, photovoltaics, and pumped storage is constructed. A tiered carbon emission trading mechanism is introduced, a comprehensive operating cost minimization objective is formulated, and the joint dispatch problem is modeled as a Markov decision process (MDP). The problem faces challenges such as multi-source stochasticity, non-convexity of tiered carbon costs, and integer variables arising from pumped-storage mode switching. Traditional stochastic programming and mixed-integer methods struggle to balance solution efficiency with online real-time perfor-mance, whereas the combination of MDP and deep rein-forcement learning effectively overcomes these bottlenecks. Based on the standard twin delayed deep deterministic policy gradient (TD3) algorithm, two enhancements—prioritized experience replay and adaptive exploration noise decay—are introduced to improve convergence speed and policy ro-bustness in continuous dispatch spaces. Simulation experi-ments using measured data from the Northeast China power system in 2024 show that on a typical high-VRE penetration day (wind capacity factor 0.34, solar capacity factor 0.26), the daily comprehensive operating cost is reduced by 35.496 million CNY, CO? emissions are cut by 13,100 tons, and the renewable energy curtailment rate drops from 8.0% to 2.5%. The rated capacity utilization of the pumped storage station reaches 93.2%, and its peak-shaving contribution rate reaches 62.3%. The improved TD3 algorithm reduces the number of convergence episodes by 24.6% compared to standard TD3 and by 52.4% compared to deep deterministic policy gradient (DDPG).