<p>Serverless architecture enables the execution of intelligent applications without the management of underlying infrastructure. As cloud services continue to evolve, containers are proving to be a full and efficient solution in virtualization technology for serverless environments. However, resource allocation in serverless systems becomes more complex due to the inappropriate scheduling of functions. Moreover, response latency is another crucial aspect of the serverless paradigm which causes a significant impact on the performance. Therefore, to tackle the aforementioned challenges and to balance the trade-off between cost, resource utilization, and response time in serverless clusters, a multi-agent deep reinforcement learning model for function scheduling (MDRL-FP) utilizing the Proximal Policy Optimization (PPO) is proposed. The MDRL-FP model addresses the aforementioned challenges by considering Virtual Machine (VM) overheads, types, and constraints. The MDRL-FP model identifies load patterns and VM categories from historical data traces to generate efficient allocation policies. The results achieved through experiments show that the proposed model achieves a reduction of 2-24% approx in cost with improved core and memory utilization as 9-34% and 10-34% respectively, compared to the state-of-the-art approaches.</p>