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World Models

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A World Model is a generative recurrent neural network that is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. Ha and Schmidhuber achieve state-of-the-art results for OpenAI Gym environments such as CarRacing-v0 by evolving a simple policy that uses these compressed representations.

In our talk, we will give an introduction to Markov Decision Processes and Model-based reinforcement learning (RL). Then we dissect the Ha and Schmidhuber paper and describe more recent work expanding on these ideas.

This talk is part of the Machine Learning Reading Group @ CUED series.

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