A hybrid expert reinforcement learning method and system, which utilize a multi-head agent with shared network parameters as a plurality of experts and train the agent by means of a deep deterministic strategy gradient algorithm so as to learn a series of similar tasks at the same time. Each expert is introduced into an uncertainty estimate of performing an action in a state to enhance the anti-overfit Q-value assessment capability and the overall performance of the model. These enable agents to extract, migrate, and share learned knowledge (feature expressions) among different tasks, thereby improving learning efficiency of a single task and effectiveness of scheduling experts among a plurality of tasks. Different from the traditional data-driven design of a hybrid expert system, the hybrid expert system adopts a self-supervised gating network to determine an expert with the highest potential to process each interaction of an unknown task, and can completely calibrate the scheduling accuracy through uncertainty estimation fed back by the expert system under the condition of no man-made external supervision information.
HYBRID EXPERT REINFORCEMENT LEARNING METHOD AND SYSTEM