  A computing cluster that interconnects multiple compute nodes is used to
accelerate distributed reinforcement learning based on DQN (Deep Q-Network). In
distributed reinforcement learning, Actor nodes acquire experiences by
interacting with a given environment and a Learner node optimizes their DQN
model. Since data transfer between Actor and Learner nodes increases depending
on the number of Actor nodes and their experience size, communication overhead
between them is one of major performance bottlenecks. In this paper, their
communication is accelerated by DPDK-based network optimizations, and
DPDK-based low-latency experience replay memory server is deployed between
Actor and Learner nodes interconnected with a 40GbE (40Gbit Ethernet) network.
Evaluation results show that, as a network optimization technique, kernel
bypassing by DPDK reduces network access latencies to a shared memory server by
32.7% to 58.9%. As another network optimization technique, an in-network
experience replay memory server between Actor and Learner nodes reduces access
latencies to the experience replay memory by 11.7% to 28.1% and communication
latencies for prioritized experience sampling by 21.9% to 29.1%.
