Background

Most of the recent breakthroughs in Deep Reinforcement Learning has been through advanced methods to collect and process data. This can be seen in the initial DQN which through the experience replay enabled the first DRL solution to a vast range of ATARI games. Later on, methods such as Prioritised Experience Replay, Distributed Experience Replay and more unique data collection methods such as Go-Explore - each of these methods has led to state-of-the-art performance in various games.