Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks

Author:

Herrmann Vincent,Kirsch Louis,Schmidhuber Jürgen

Publisher

Springer Nature Switzerland

Reference83 articles.

1. Berner, C., et al.: Dota 2 with large scale deep reinforcement learning. CoRR abs/1912.06680 (2019). http://arxiv.org/abs/1912.06680

2. Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., Efros, A.A.: Large-scale study of curiosity-driven learning. Preprint arXiv:1808.04355 (2018)

3. Faccio, F., Herrmann, V., Ramesh, A., Kirsch, L., Schmidhuber, J.: Goal-conditioned generators of deep policies. arXiv preprint arXiv:2207.01570 (2022)

4. Faccio, F., Kirsch, L., Schmidhuber, J.: Parameter-based value functions. Preprint arXiv:2006.09226 (2020)

5. Faccio, F., Ramesh, A., Herrmann, V., Harb, J., Schmidhuber, J.: General policy evaluation and improvement by learning to identify few but crucial states. arXiv preprint arXiv:2207.01566 (2022)

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