Recurrent World Models Facilitate Policy Evolution

NeurIPS 2018 David HaJürgen Schmidhuber

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... (read more)

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