A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. We also train our agent entirely inside of an environment generated by its own internal world model, and transfer this policy back into the actual environment. Interactive version of this paper is available at https://worldmodels.github.io

Plus: AI fakes of the Iran war are flooding X--and Grok is failing to flag them. Pokémon Go was the world's first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. "500 million people installed that app in 60 days," says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year. Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model--a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely.

Recently, Model-based Reinforcement Learning (MBRL) methods have demonstrated stunning sample efficiency in various RL domains.

SeqComm are guaranteed to improve monotonically and converge.

However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions.

Training offline RL models using visual inputs poses two significant challenges, i.e.,