Evolving Recurrent Dynamical Networks for Robot Control

By D. Cliff, P. Husbands, Inman R. Harvey. 1993

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1. J. Urgen Schmidhuber
2. David R Ha
3. Yoshua Bengio
4. Sergey Levine
5. Quoc V. Le
6. Chelsea Finn
7. Alex Graves
8. Trevor Darrell

Citations in the corpus

• On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1 RL through Direct and Indirect Search in RNN Program Space It is possible to train small RNNs with a few 100 or 1000 weights using evolutionary algorithms [200, 255, 105, 56, 68] to search the space of NN weights [165, 307, 44, 321, 180, 259, 320, 164, 173, 69, 71, 187, 121, 313, 66, 270, 269, 305”