Showing 1–17 of 17 results
/ Date/ Name
Jul 2, 2019Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree PolicyMay 28, 2019Generation of Policy-Level Explanations for Reinforcement LearningFeb 25, 2021Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable MethodsJul 5, 2021The MineRL BASALT Competition on Learning from Human FeedbackJun 7, 2021Towards robust and domain agnostic reinforcement learning competitionsFeb 17, 2022MineRL Diamond 2021 Competition: Overview, Results, and Lessons LearnedMay 12, 2020Guaranteeing Reproducibility in Deep Learning CompetitionsFeb 14, 2017Exploring loss function topology with cyclical learning ratesNov 2, 2016Deep Convolutional Neural Network Design PatternsMar 10, 2020Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement LearningMay 25, 2022MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement LearningJul 29, 2019MineRL: A Large-Scale Dataset of Minecraft DemonstrationsAug 23, 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning RatesApr 22, 2019The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsJan 26, 2021The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human PriorsJun 5, 2022Use-Case-Grounded Simulations for Explanation EvaluationFeb 17, 2022A Survey of Explainable Reinforcement Learning