Alessandro De Maria

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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.(More)
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