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Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks.

, , , and . NeurIPS, page 10943-10953. (2019)

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A formal framework for robot learning and control under model uncertainty., , and . ICRA, page 2104-2110. IEEE, (2007)Hierarchical Control of Markov Decision Processes, , , , and . Proceedings of the Tenth Yale Workshop on Adaptive and Learning Systems, page 186--191. (1998)Bisimulation Metrics for Continuous Markov Decision Processes., , and . SIAM J. Comput., 40 (6): 1662-1714 (2011)Probabilistic Constraint Programming, , , , , , and . Working Notes of the 2014 NIPS Workshop on Probabilistic Programming, (2014)Constructing Temporal Abstractions Autonomously in Reinforcement Learning, and . AI Magazine, 39 (1): 39--50 (March 2018)Methods for computing state similarity in Markov Decision Processes., , , and . CoRR, (2012)OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning., , , , , and . CoRR, (2017)Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control., , , and . CoRR, (2017)Investigating Recurrence and Eligibility Traces in Deep Q-Networks., and . CoRR, (2017)An Empirical Analysis of Off-policy Learning in Discrete MDPs., , , and . EWRL, volume 24 of JMLR Proceedings, page 89-102. JMLR.org, (2012)