I am a first-year Ph.D. student at the Empirical Inference department, supervised by Bernhard Schölkopf. Currently, I am researching methods to make reinforcement learning agents discover and explore causal mechanisms in their environments. My goal is to help to make reinforcement learning more robust and sample-efficient.
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Parascandolo*, G., Buesing*, L., Merel, J., Hasenclever, L., Aslanides, J., Hamrick, J. B., Heess, N., Neitz, A., Weber, T.
Divide-and-Conquer Monte Carlo Tree Search for goal directed planning
2020, *equal contribution (conference) Submitted
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Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32nd Annual Conference on Neural Information Processing Systems, December 2018 (conference)
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Schmid, K., Belzner, L., Kiermeier, M., Neitz, A., Phan, T., Gabor, T., Linnhoff, C.
Risk-Sensitivity in Simulation Based Online Planning
KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, pages: 229-240, (Editors: F. Trollmann and A. Y. Turhan), Springer, Cham, September 2018 (conference)
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