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Deterministic Policy Gradient Algorithms.

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller. ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 387-395. JMLR.org, (2014)

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Nicolas Huber University of Stuttgart

Nicolas Kroneberg University of Stuttgart

Nicolas Möbs University of Stuttgart

Nicolas Schneider University of Stuttgart

Nicolas Hornek University of Stuttgart

Other publications of authors with the same name

Learning to Pass Expectation Propagation Messages.N. Heess, D. Tarlow, and J. Winn. NIPS, page 3219-3227. (2013)Bayes-Adaptive Simulation-based Search with Value Function Approximation.A. Guez, N. Heess, D. Silver, and P. Dayan. NIPS, page 451-459. (2014)Learning a Generative Model of Images by Factoring Appearance and Shape.N. Roux, N. Heess, J. Shotton, and J. Winn. Neural Computation, 23 (3): 593-650 (2011)Gradient Estimation Using Stochastic Computation Graphs.J. Schulman, N. Heess, T. Weber, and P. Abbeel. CoRR, (2015)Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.G. Lorberbom, C. Maddison, N. Heess, T. Hazan, and D. Tarlow. CoRR, (2019)Recurrent Models of Visual Attention.V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu. CoRR, (2014)Value constrained model-free continuous control.S. Bohez, A. Abdolmaleki, M. Neunert, J. Buchli, N. Heess, and R. Hadsell. CoRR, (2019)FeUdal Networks for Hierarchical Reinforcement Learning.A. Vezhnevets, S. Osindero, T. Schaul, N. Heess, M. Jaderberg, D. Silver, and K. Kavukcuoglu. ICML, volume 70 of Proceedings of Machine Learning Research, page 3540-3549. PMLR, (2017)The Termination Critic.A. Harutyunyan, W. Dabney, D. Borsa, N. Heess, R. Munos, and D. Precup. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 2231-2240. PMLR, (2019)Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures.J. Uesato, A. Kumar, C. Szepesvári, T. Erez, A. Ruderman, K. Anderson, K. Dvijotham, N. Heess, and P. Kohli. ICLR (Poster), OpenReview.net, (2019)