Arun Nair; Praveen Srinivasan; Sam Blackwell; Cagdas Alcicek; Rory Fearon; Alessandro De Maria; Vedavyas Panneershelvam; Mustafa Suleyman; Charles Beattie; Stig Petersen; Shane Legg; Volodymyr Mnih; Koray Kavukcuoglu; David Silver

Abstract

We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN). Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.