Making the World Di erentiable: On Using Self-Supervised Fully Recurrent Neural Networks for Dynamic Reinforcement Learning

By J. Urgen Schmidhuber. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Nicholas Watters
2. Loic Matthey
3. Christopher P. Burgess
4. J. Urgen Schmidhuber
5. Francesco Locatello
6. Alexander Lerchner
7. Fei Ye
8. Yoshua Bengio

Citations in the corpus

• COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration (2019, positive): “This approach is a form of curiosity-driven exploration, as previously described in both the psychology (Gopnik et al., 1999) and the reinforcement learning literature (Pathak et al., 2017; Schmidhuber, 1990a,b).”
• Continual learning in reinforcement environments (1995, methodology): “Another constructive , bottom - up approach is the \hierarchy of decisions " of Dawkins 18 ] , similar to \history compression , " recently described and implemented by Schmidhuber 96 ] .”