Mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads and decrease service latency for computationintensive applications by offloading the tasks to MEC servers. However, the existing computation offloading and resource allocation studies present some several problems: poor collaboration between edge servers; mismatch between the computational task arrival and the dynamic characteristics in the real environment; and the dynamic joint optimization problem of the collaborative task unload and resource allocation. To solve such issues, based on the collaborative MEC framework, a multiagent based deep deterministic policy gradient (MADDPG) is proposed for task unloading and resource allocation to minimize the overall longterm average cost. Simulation results reveal that the proposed scheme can reduce the delay and energy consumption.