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Dead-ends and Secure Exploration in Reinforcement Learning.

M. Fatemi, S. Sharma, H. van Seijen, and S. Kahou. ICML, volume 97 of Proceedings of Machine Learning Research, page 1873-1881. PMLR, (2019)

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Anna Harman University of Stuttgart

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Dr. Thijs van Westen University of Stuttgart

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True Online Temporal-Difference Learning.H. van Seijen, A. Mahmood, P. Pilarski, M. Machado, and R. Sutton. CoRR, (2015)Dead-ends and Secure Exploration in Reinforcement Learning.M. Fatemi, S. Sharma, H. van Seijen, and S. Kahou. ICML, volume 97 of Proceedings of Machine Learning Research, page 1873-1881. PMLR, (2019)Switching between Representations in Reinforcement Learning.H. van Seijen, S. Whiteson, and L. Kester. Interactive Collaborative Information Systems, volume 281 of Studies in Computational Intelligence, Springer, (2010)Learning Invariances for Policy Generalization.R. des Combes, P. Bachman, and H. van Seijen. ICLR (Workshop), OpenReview.net, (2018)Improving Scalability of Reinforcement Learning by Separation of Concerns.H. van Seijen, M. Fatemi, J. Romoff, and R. Laroche. CoRR, (2016)Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation.H. van Seijen. CoRR, (2016)Efficient Abstraction Selection in Reinforcement Learning (Extended Abstract).H. van Seijen, S. Whiteson, and L. Kester. SARA, AAAI, (2013)Postponed Updates for Temporal-Difference Reinforcement Learning.H. van Seijen, and S. Whiteson. ISDA, page 665-672. IEEE Computer Society, (2009)Efficient Abstraction Selection in Reinforcement Learning.H. van Seijen, S. Whiteson, and L. Kester. Computational Intelligence, 30 (4): 657-699 (2014)Exploiting Best-Match Equations for Efficient Reinforcement Learning.H. van Seijen, S. Whiteson, H. van Hasselt, and M. Wiering. J. Mach. Learn. Res., (2011)