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Provable benefits of general coverage conditions in efficient online RL with function approximation.

, , and . CoRR, (2023)

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Provable benefits of general coverage conditions in efficient online RL with function approximation., , and . CoRR, (2023)Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration., , and . CoRR, (2022)Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration., , and . NeurIPS, (2022)Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch., , , , and . NeurIPS, page 25917-25931. (2021)Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning., , , , and . NeurIPS, (2022)What can online reinforcement learning with function approximation benefit from general coverage conditions?, , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 22063-22091. PMLR, (2023)Neural NID Rules., and . CoRR, (2022)Polynomial Convergence of Bandit No-Regret Dynamics in Congestion Games., , , , and . CoRR, (2024)Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 26904-26930. PMLR, (2023)Proximal Point Imitation Learning., , , , and . NeurIPS, (2022)