An apparatus for distributed reinforcement learning includes: a local neural network for receiving state information regarding a surrounding environment and estimating an action execution probability from the state information according to a previously learned pattern estimation method; a loss estimation unit for applying learning to the local neural network by estimating a loss value from the action execution probability and a global action execution probability transmitted from a central server; a local experience memory for mapping and storing the state information and the action execution probability; a clustering unit for clustering the state information stored in the local experience memory according to a pre-designated method to classify the state information into state clusters having proxy state information configured beforehand; and a local proxy memory for mapping and storing the proxy state information and proxy action execution probability corresponding to each state cluster for transmitting to the central server.
APPARATUS AND METHOD FOR DISTRIBUTED REINFORCEMENT LEARNING