R2D2
Recurrent Replay Distributed DQN
Building on the recent successes of distributed training of RL agents, R2D2 is an RL approach that trains a RNN-based RL agents from distributed prioritized experience replay. 
Using a single network architecture and fixed set of hyperparameters, Recurrent Replay Distributed DQN quadrupled the previous state of the art on Atari-57, and matches the state of the art on DMLab-30. 
It was the first agent to exceed human-level performance in 52 of the 57 Atari games.
Recurrent Experience Replay in Distributed Reinforcement Learning
https://paperswithcode.com/paper/recurrent-experience-replay-in-distributed
Recurrent Experience Replay in Distributed Reinforcement Learning