tabular reinforcement learning

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning NeurIPS 2024 Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning AISTATS 2023 Hardness in Markov Decision Processes: Theory and Practice NeurIPS 2022 Nearly Optimal Policy Optimization with Stable at Any Time Guarantee ICML 2022 Near-Optimal Randomized Exploration for Tabular Markov Decision Processes NeurIPS 2022 Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning AISTATS 2021 Q-learning with Logarithmic Regret AISTATS 2021 Periodic Q-Learning L4DC 2020 A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019 Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs NeurIPS 2019 #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning NeurIPS 2017 Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects IJCAI 2017