Mobile edge computing (MEC) enables mobile devices (MDs) to offload tasks or applications to MEC servers for processing. As a MEC server consumes local resources when processing external tasks, it is important to build a multi-resource pricing mechanism that charges MDs to reward MEC servers. Existing pricing mechanisms rely on the static pricing of intermediaries. The highly dynamic nature of tasks makes it extremely difficult to effectively utilize edge-cloud computing resources. To address this problem, we propose a Stackelberg game-based framework in which MEC servers and an aggregation platform (AP) act as followers and the leader, respectively. We decompose the multi-resource allocation and pricing problem into a set of subproblems, with each subproblem only considering a single resource type. First, with the unit prices announced by MEC servers, the AP calculates the quantity of resources for each MD to purchase from each MEC server by solving a convex optimization problem. Then, each MEC server calculates its trading records and iteratively adjusts its pricing strategy with a multi-agent proximal policy optimization (MAPPO) algorithm. The simulation results show that MAPPO outperforms a number of state-of-the-art deep reinforcement learning algorithms in terms of payoff and welfare.