finite-time convergence

A Theoretical Justification for Asymmetric Actor-Critic Algorithms ICML 2025 Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning ICML 2025 Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis AISTATS 2025 On the Convergence of Continuous Single-timescale Actor-critic ICML 2025 Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning ICML 2024 Finite-Time Convergence Rates of Decentralized Local Markovian Stochastic Approximation IJCAI 2024 Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence NeurIPS 2023 Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters JMLR 2023 HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent AISTATS 2023 On Penalty-based Bilevel Gradient Descent Method ICML 2023 A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences NeurIPS 2022 Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward ICLR 2022 Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization NeurIPS 2022 Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies ICML 2021 A decentralized policy gradient approach to multi-task reinforcement learning UAI 2021 Multi-Agent Reinforcement Learning in Stochastic Networked Systems NeurIPS 2021 Finite-Time Convergence in Continuous-Time Optimization ICML 2020 Are we there yet? Manifold identification of gradient-related proximal methods AISTATS 2019