R2D2

2000년 도입 · 논문 25편에서 사용

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.

출처: Recurrent Experience Replay in Distributed Reinforcement Learning

소개 논문: Recurrent Experience Replay in Distributed Reinforcement Learning