Disclosed is an efficient learning method for a multi-agent deep reinforcement learning algorithm. According to an embodiment of the present invention, the learning method for a multi-agent deep reinforcement learning algorithm comprises the following steps of: determining an action by using a policy of inputting observation information and a received message of each of a plurality of agents; and training a network by dropping out the received message according to a drop rate determined for message dropout based on the determined action, wherein a reinforcement learning method may be for multi-agent reinforcement learning in 1) a decentralized control with communication environment or 2) a centralized training with decentralized execution environment.
: MESSAGE-DROPOUT: AN EFFICIENT TRAINING METHOD FOR MULTI-AGENT DEEP REINFORCEMENT LEARNING