Model-Based Reinforcement Learning for Atari

arXiv:1903.00374v5 Announce Type: replace
Abstract: Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. …

abstract arxiv atari games cs.lg free games however human image learn people policies reinforcement reinforcement learning stat.ml tasks type