World Models

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Authors David Ha, J├╝rgen Schmidhuber
Journal/Conference Name Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Paper Category
Paper Abstract We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https//worldmodels.github.io/
Date of publication 2018
Code Programming Language Multiple
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