{"total":122,"rows":[{"id":"2109.14523","title":"Online Robust Reinforcement Learning with Model Uncertainty","created":"2021-09-29","cited_by_count":96,"area":null},{"id":"2102.06548","title":"Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis","created":"2021-02-12","cited_by_count":92,"area":null},{"id":"2102.01567","title":"A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants","created":"2021-02-02","cited_by_count":57,"area":null},{"id":"2102.01168","title":"Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges","created":"2021-01-27","cited_by_count":50,"area":null},{"id":"2105.01424","title":"On the Linear convergence of Natural Policy Gradient Algorithm","created":"2021-05-04","cited_by_count":48,"area":null},{"id":"2207.04475","title":"Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation","created":"2022-07-10","cited_by_count":41,"area":null},{"id":"2106.00198","title":"Gradient play in stochastic games: stationary points, convergence, and sample complexity","created":"2021-06-01","cited_by_count":34,"area":null},{"id":"2110.14427","title":"The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning","created":"2021-10-27","cited_by_count":32,"area":null},{"id":"2203.02628","title":"Target Network and Truncation Overcome The Deadly Triad in $Q$-Learning","created":"2022-03-05","cited_by_count":31,"area":null},{"id":"2210.00953","title":"Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes","created":"2022-10-03","cited_by_count":30,"area":null},{"id":"2106.01257","title":"Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize","created":"2021-06-02","cited_by_count":27,"area":null},{"id":"2302.02212","title":"Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity","created":"2023-02-04","cited_by_count":27,"area":null},{"id":"2102.09318","title":"Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm","created":"2021-02-18","cited_by_count":26,"area":null},{"id":"2108.03706","title":"Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning","created":"2021-08-08","cited_by_count":26,"area":null},{"id":"2303.15740","title":"Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise","created":"2023-03-28","cited_by_count":26,"area":null},{"id":"2203.02591","title":"A Small Gain Analysis of Single Timescale Actor Critic","created":"2022-03-04","cited_by_count":24,"area":null},{"id":"2102.00185","title":"On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning","created":"2021-01-30","cited_by_count":23,"area":null},{"id":"2106.14308","title":"Concentration of Contractive Stochastic Approximation and Reinforcement Learning","created":"2021-06-27","cited_by_count":23,"area":null},{"id":"2210.05918","title":"Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation","created":"2022-10-12","cited_by_count":23,"area":null},{"id":"2105.12540","title":"Finite-Sample Analysis of Off-Policy Natural Actor-Critic with Linear Function Approximation","created":"2021-05-26","cited_by_count":22,"area":null},{"id":"2206.00833","title":"Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm","created":"2022-06-02","cited_by_count":20,"area":null},{"id":"2208.11639","title":"Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path","created":"2022-08-24","cited_by_count":20,"area":null},{"id":"2310.14286","title":"Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability","created":"2023-10-22","cited_by_count":20,"area":null},{"id":"2110.08440","title":"Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs","created":"2021-10-16","cited_by_count":19,"area":null},{"id":"2112.13109","title":"Accelerated and instance-optimal policy evaluation with linear function approximation","created":"2021-12-24","cited_by_count":18,"area":null},{"id":"2106.12729","title":"Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators","created":"2021-06-24","cited_by_count":17,"area":null},{"id":"2110.11383","title":"Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes","created":"2021-10-21","cited_by_count":17,"area":null},{"id":"2206.10414","title":"A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences","created":"2022-06-21","cited_by_count":17,"area":null},{"id":"2401.15719","title":"Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning","created":"2024-01-28","cited_by_count":17,"area":null},{"id":"2305.19001","title":"High-probability sample complexities for policy evaluation with linear function approximation","created":"2023-05-30","cited_by_count":16,"area":null},{"id":"2403.02476","title":"A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation","created":"2024-03-04","cited_by_count":16,"area":null},{"id":"2202.06828","title":"On the Convergence of SARSA with Linear Function Approximation","created":"2022-02-14","cited_by_count":15,"area":null},{"id":"2305.08104","title":"Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling","created":"2023-05-14","cited_by_count":15,"area":null},{"id":"2312.02828","title":"Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications","created":"2023-12-05","cited_by_count":15,"area":null},{"id":"2401.00364","title":"Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise","created":"2023-12-31","cited_by_count":15,"area":null},{"id":"2106.07472","title":"Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation","created":"2021-06-14","cited_by_count":14,"area":null},{"id":"2305.15938","title":"First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities","created":"2023-05-25","cited_by_count":14,"area":null},{"id":"2310.02147","title":"Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation","created":"2023-10-03","cited_by_count":14,"area":null},{"id":"2110.11280","title":"Actor-critic is implicitly biased towards high entropy optimal policies","created":"2021-10-21","cited_by_count":13,"area":null},{"id":"2111.06328","title":"Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization","created":"2021-11-11","cited_by_count":13,"area":null},{"id":"2302.12537","title":"Why Target Networks Stabilise Temporal Difference Methods","created":"2023-02-24","cited_by_count":13,"area":null},{"id":"2301.00944","title":"Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning","created":"2023-01-03","cited_by_count":12,"area":null},{"id":"2103.01391","title":"Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation","created":"2021-03-02","cited_by_count":10,"area":null},{"id":"2104.01627","title":"Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise","created":"2021-04-04","cited_by_count":10,"area":null},{"id":"2108.05338","title":"Truncated Emphatic Temporal Difference Methods for Prediction and Control","created":"2021-08-11","cited_by_count":10,"area":null},{"id":"2312.10424","title":"A Concentration Bound for TD(0) with Function Approximation","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2405.17834","title":"Revisiting Step-Size Assumptions in Stochastic Approximation","created":"2024-05-28","cited_by_count":10,"area":null},{"id":"2409.19546","title":"Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise","created":"2024-09-29","cited_by_count":10,"area":null},{"id":"2301.09709","title":"On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation","created":"2023-01-23","cited_by_count":9,"area":null},{"id":"2303.04865","title":"Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games","created":"2023-03-08","cited_by_count":9,"area":null},{"id":"2305.02456","title":"Streaming PCA for Markovian Data","created":"2023-05-03","cited_by_count":9,"area":null},{"id":"2405.14285","title":"Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise","created":"2024-05-23","cited_by_count":9,"area":null},{"id":"2104.02836","title":"Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation","created":"2021-04-07","cited_by_count":8,"area":null},{"id":"2103.16377","title":"Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity","created":"2021-03-30","cited_by_count":7,"area":null},{"id":"2308.01170","title":"Revisiting a Design Choice in Gradient Temporal Difference Learning","created":"2023-08-02","cited_by_count":7,"area":null},{"id":"2308.01481","title":"Online covariance estimation for stochastic gradient descent under Markovian sampling","created":"2023-08-03","cited_by_count":7,"area":null},{"id":"2401.09339","title":"Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications","created":"2024-01-17","cited_by_count":7,"area":null},{"id":"2401.12764","title":"Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving $O(1/k)$ Finite-Sample Complexity","created":"2024-01-23","cited_by_count":7,"area":null},{"id":"2402.04114","title":"SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning","created":"2024-02-06","cited_by_count":7,"area":null},{"id":"2206.11346","title":"Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data","created":"2022-06-22","cited_by_count":6,"area":null},{"id":"2502.09884","title":"Nonasymptotic CLT and Error Bounds for Linear Two-Time-Scale Stochastic Approximation","created":"2025-02-14","cited_by_count":6,"area":null},{"id":"2502.14172","title":"A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation","created":"2025-02-20","cited_by_count":6,"area":null},{"id":"2103.13147","title":"Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity","created":"2021-03-24","cited_by_count":5,"area":null},{"id":"2312.10894","title":"Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference","created":"2023-12-18","cited_by_count":5,"area":null},{"id":"2410.13067","title":"Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way","created":"2024-10-16","cited_by_count":5,"area":null},{"id":"2112.00882","title":"Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes","created":"2021-12-01","cited_by_count":4,"area":null},{"id":"2403.15935","title":"Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update","created":"2024-03-23","cited_by_count":4,"area":null},{"id":"2505.21796","title":"A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging","created":"2025-05-27","cited_by_count":4,"area":null},{"id":"2509.18964","title":"Central Limit Theorems for Asynchronous Averaged Q-Learning","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2510.06540","title":"Scalable Policy-Based RL Algorithms for POMDPs","created":"2025-10-08","cited_by_count":4,"area":null},{"id":"2510.12375","title":"Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation","created":"2025-10-14","cited_by_count":4,"area":null},{"id":"2202.06922","title":"Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods","created":"2022-02-14","cited_by_count":3,"area":null},{"id":"2208.03247","title":"An Approximate Policy Iteration Viewpoint of Actor-Critic Algorithms","created":"2022-08-05","cited_by_count":3,"area":null},{"id":"2209.02555","title":"Finite-Time Error Bounds for Greedy-GQ","created":"2022-09-06","cited_by_count":3,"area":null},{"id":"2306.05700","title":"Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach","created":"2023-06-09","cited_by_count":3,"area":null},{"id":"2409.12882","title":"On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks","created":"2024-09-19","cited_by_count":3,"area":null},{"id":"2504.08178","title":"A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression","created":"2025-04-11","cited_by_count":3,"area":null},{"id":"2508.07001","title":"Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization","created":"2025-08-09","cited_by_count":3,"area":null},{"id":"2602.02445","title":"Finite-Sample Wasserstein Error Bounds and Concentration Inequalities for Nonlinear Stochastic Approximation","created":"2026-02-02","cited_by_count":3,"area":null},{"id":"2110.06906","title":"PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method","created":"2021-10-13","cited_by_count":2,"area":null},{"id":"2212.06357","title":"Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems","created":"2022-12-13","cited_by_count":2,"area":null},{"id":"2303.07068","title":"n-Step Temporal Difference Learning with Optimal n","created":"2023-03-13","cited_by_count":2,"area":null},{"id":"2403.17247","title":"DASA: Delay-Adaptive Multi-Agent Stochastic Approximation","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2404.10575","title":"EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence","created":"2024-04-16","cited_by_count":2,"area":null},{"id":"2405.07432","title":"Nonparametric Sparse Online Learning of the Koopman Operator","created":"2024-05-13","cited_by_count":2,"area":null},{"id":"2601.01502","title":"Multiscale replay: A robust algorithm for stochastic variational inequalities with a Markovian buffer","created":"2026-01-04","cited_by_count":2,"area":null},{"id":"2606.24981","title":"A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak--Ruppert Averaging","created":"2026-06-23","cited_by_count":2,"area":null},{"id":"2107.02711","title":"A Unified Off-Policy Evaluation Approach for General Value Function","created":"2021-07-06","cited_by_count":1,"area":null},{"id":"2111.12665","title":"Finite-Time Error Bounds for Distributed Linear Stochastic Approximation","created":"2021-11-24","cited_by_count":1,"area":null},{"id":"2211.16237","title":"Closing the gap between SVRG and TD-SVRG with Gradient Splitting","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2306.09746","title":"Finite-Time Analysis of Temporal Difference Learning with Experience Replay","created":"2023-06-16","cited_by_count":1,"area":null},{"id":"2310.00638","title":"A primal-dual perspective for distributed TD-learning","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2405.17463","title":"Is Thompson Sampling Susceptible to Algorithmic Collusion?","created":"2024-05-23","cited_by_count":1,"area":null},{"id":"2508.05570","title":"High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation","created":"2025-08-07","cited_by_count":1,"area":null},{"id":"2509.19901","title":"Pure Exploration via Frank-Wolfe Self-Play","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2510.05023","title":"Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective","created":"2025-10-06","cited_by_count":1,"area":null},{"id":"2605.15530","title":"Rethinking Neural Network Learning Rates: A Stackelberg Perspective","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2608.10896","title":"Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling","created":"2026-08-11","cited_by_count":1,"area":null},{"id":"2608.15966","title":"A Banach-Space Theory of Markovian Halpern Iteration for Non-Expansive Maps","created":"2026-08-16","cited_by_count":1,"area":null},{"id":"2609.38880","title":"Near-Minimax-Optimal Rates for Asynchronous TD Learning with Markovian Data","created":"2026-09-30","cited_by_count":1,"area":null}],"next_cursor":"100"}