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.
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