A method and a system for federated reinforcement learning based offloading optimization in edge computing are provided. In the method, each user equipment inputs network and task states into an actor network to generate an actor weighting table, accordingly selects an action for executing the task and obtains an evaluation. The related data is stored as experience in a replay buffer. Some experiences are extracted from the replay buffer, input into a critic network to obtain a value function, and input into a target actor network and a target critic network in order, to obtain a target value function, which are used to update network parameters of the actor and critic networks, and soft update network parameters of the target actor and critic networks. An average utility of learning and the actor weighting table are uploaded to cloud equipment. The cloud equipment accordingly computes a global weighting table and replies the same to user equipment for updating the actor weighting table. |