A decision-making method based on deep reinforcement learning, the method comprising: an intelligent agent makes a decision according to environment information and selects an action after the decision; the intelligent agent compares said action with a knowledge base, and decides, on the basis of a set ruleset in the knowledge base, to execute said action or to replace said action; the intelligent agent executes said action or the replaced action in the environment, obtains a reward and new environment information from the environment, combines the old environment information, the action, the reward and the new environment information into experience information, and stores the experience information into an experience playback pool; and a set amount of experience information is randomly selected from the experience playback pool, so as to update a deep reinforcement learning model to thereby guide the next iteration. By utilizing the present application, training time may be shortened, catastrophic decision-making may be avoided, and the method may be widely applied to the field of dynamic decision-making.
DECISION-MAKING METHOD BASED ON DEEP REINFORCEMENT LEARNING