We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.
A distributed architecture for deep reinforcement learning at scale, using parallel data-generation to improve the state of the art on the Arcade Learning Environment benchmark in a fraction of the wall-clock training time of previous approaches. Examines a distirbuted Deep RL system in which experiences, rather than gradients, are shared between the parallel works and the cetralized learner A parallel approach to DQN training, based on the idea of having multiple actors collecting data in parallel while a single learner trains the model from experiences sampled from central replay memory. This paper proposes a distributed architecture for deep reinforcement learning at scale, focusing on adding parallelization in actor algorithm in Prioritized Experience Replay framework