Provided is a method for exploration based on curiosity and prioritization of experience data in multi-agent reinforcement learning, the method including the steps of: calculating a similarity between a policy of a first agent and a policy of a second agent and computing a final reward using the similarity; and performing clustering on a replay buffer using a result of calculating the similarity between the policy of the first agent and the policy of the second agent and performing sampling on data in the cluster.
METHOD FOR EXPLORATION BASED ON CURIOSITY AND PRIORITIZATION OF EXPERIENCE DATA IN MULTI-AGENT REINFORCEMENT LEARNING