An Efficient Distributed Reinforcement Learning Architecture for Long-Haul Communication Between Actors and Learner
Shin Morishima,
Hiroki Matsutani
Abstract:A computing cluster that interconnects multiple compute nodes is used to accelerate distributed reinforcement learning that uses DQN (Deep Q-Network). In distributed reinforcement learning, actor nodes acquire experiences by interacting with a given environment and a learner node optimizes the DQN model. When distributed reinforcement learning is used in practical applications such as robotics, we can assume that actor nodes are located in edge side while the learner node is located in cloud side. In this case… Show more
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