Model Based Reinforcement Learning for Atari

ICLR 2020 Łukasz KaiserMohammad BabaeizadehPiotr MiłosBłażej OsińskiRoy H CampbellKonrad CzechowskiDumitru ErhanChelsea FinnPiotr KozakowskiSergey LevineAfroz MohiuddinRyan SepassiGeorge TuckerHenryk Michalewski

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... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet