Recurrent World Models Facilitate Policy Evolution

Understand

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 paper at https://worldmodels.github.io