The paper proposes a robot adaptive admittance control method based on the multi-agent deep deterministic policy gradient (MA-DDPG) to address the issue of low trajectory accuracy in fixed-parameter active compliance control caused by modeling errors, such as uncertainty in robot internal parameters. Firstly, an admittance controller is established based on the robot model. Secondly, by integrating the DDPG algorithm with the admittance control framework, an adaptive admittance controller is developed, wherein the DDPG-based agent dynamically generates optimal admittance parameters. To address issues of slow convergence and unsatisfactory control performance, the concept of multiple agents is introduced into the adaptive admittance control algorithm, with each agent responsible for optimizing an individual admittance control parameter. The MA-DDPG algorithm, based on a centralized training and distributed execution architecture, is employed to optimize the admittance controller parameters. Finally, the feasibility and effectiveness of the proposed method are validated through a comparative analysis between the impact of deep reinforcement learning simulation training and the experimental outcomes of adaptive admittance control on the anticipated trajectory. The experimental data demonstrate that in comparison with adaptive admittance control based on alternative deep reinforcement learning algorithms, the proposed method exhibits a 65.88% improvement in convergence speed and a 63.35% enhancement in trajectory accuracy.