Quantifying RL Policies and EnvironmentsTRAIL AdminJun 19, 2021Robot Learning RLTRAIL AdminAdministrator of TRAILAdministrator of TRAILRelatedAI Application Project (2023 Summer)AI Application Project (2022 Autumn)AI Application Project (2022 Summer)DRL Autumn Seminar 2021AI Application Project (2021 Winter)PostsOur Ecology Theory of RL workshop is accepted to NeurIPS 2021!Our Ecology Theory of RL workshop is accepted to NeurIPS 2021!Shixiang Shane Gu, Hiroki FurutaAug 11, 2021ProjectPublicationsCo-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement LearningRecently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic …Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane GuPDF Cite Code ProjectPolicy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningProgress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature …Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane GuCite Project
