{"total":37,"rows":[{"id":"2401.15273","title":"Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning","created":"2024-01-27","cited_by_count":31,"area":"deeprl"},{"id":"2401.13884","title":"Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation","created":"2024-01-25","cited_by_count":17,"area":"deeprl"},{"id":"2405.09999","title":"Reward Centering","created":"2024-05-16","cited_by_count":16,"area":"deeprl"},{"id":"2408.16981","title":"The Sample-Communication Complexity Trade-off in Federated Q-Learning","created":"2024-08-30","cited_by_count":9,"area":"deeprl"},{"id":"2503.18391","title":"Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise","created":"2025-03-24","cited_by_count":9,"area":"deeprl"},{"id":"2402.11800","title":"Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling","created":"2024-02-19","cited_by_count":7,"area":"deeprl"},{"id":"2511.03618","title":"Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach","created":"2025-11-05","cited_by_count":7,"area":"deeprl"},{"id":"2404.10635","title":"Compressed Federated Reinforcement Learning with a Generative Model","created":"2024-03-26","cited_by_count":6,"area":"deeprl"},{"id":"2509.26442","title":"Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning","created":"2025-09-30","cited_by_count":5,"area":"deeprl"},{"id":"2510.16132","title":"A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies","created":"2025-10-17","cited_by_count":4,"area":"deeprl"},{"id":"2403.06366","title":"Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach","created":"2024-03-11","cited_by_count":3,"area":"deeprl"},{"id":"2601.13642","title":"Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning","created":"2026-01-20","cited_by_count":3,"area":"deeprl"},{"id":"2405.16053","title":"Pausing Policy Learning in Non-stationary Reinforcement Learning","created":"2024-05-25","cited_by_count":2,"area":"deeprl"},{"id":"2409.03237","title":"Robust Q-Learning under Corrupted Rewards","created":"2024-09-05","cited_by_count":2,"area":"deeprl"},{"id":"2504.11645","title":"Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling","created":"2025-04-15","cited_by_count":2,"area":"deeprl"},{"id":"2507.14444","title":"Statistical and Algorithmic Foundations of Reinforcement Learning","created":"2025-07-19","cited_by_count":2,"area":"deeprl"},{"id":"2605.11021","title":"A Switching System Theory of Q-Learning with Linear Function Approximation","created":"2026-05-10","cited_by_count":2,"area":"deeprl"},{"id":"2405.14078","title":"A finite time analysis of distributed Q-learning","created":"2024-05-23","cited_by_count":1,"area":"deeprl"},{"id":"2509.08933","title":"Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates","created":"2025-09-10","cited_by_count":1,"area":"deeprl"},{"id":"2510.07436","title":"Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environments","created":"2025-10-08","cited_by_count":1,"area":"deeprl"},{"id":"2602.16274","title":"Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains","created":"2026-02-18","cited_by_count":1,"area":"deeprl"},{"id":"2605.07104","title":"Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift","created":"2026-05-08","cited_by_count":1,"area":"deeprl"},{"id":"2605.11289","title":"Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning","created":"2026-05-11","cited_by_count":1,"area":"deeprl"},{"id":"2605.16103","title":"Sign-Separated Asymmetric Finite-Time Error Analysis of Q-Learning","created":"2026-05-15","cited_by_count":1,"area":"deeprl"},{"id":"2605.31309","title":"Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework","created":"2026-05-29","cited_by_count":1,"area":"deeprl"},{"id":"2609.20174","title":"Robust Federated Q-Learning with Almost No Communication","created":"2026-08-24","cited_by_count":1,"area":"deeprl"},{"id":"2402.11877","title":"Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ","created":"2024-02-19","cited_by_count":0,"area":"deeprl"},{"id":"2405.02201","title":"Regularized Q-learning through Robust Averaging","created":"2024-05-03","cited_by_count":0,"area":"deeprl"},{"id":"2408.06544","title":"Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity","created":"2024-08-13","cited_by_count":0,"area":"deeprl"},{"id":"2503.13414","title":"Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes","created":"2025-03-17","cited_by_count":0,"area":"deeprl"},{"id":"2511.16226","title":"Deep SOR Minimax Q-learning for Two-player Zero-sum Game","created":"2025-11-20","cited_by_count":0,"area":"deeprl"},{"id":"2604.04218","title":"Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization","created":"2026-04-05","cited_by_count":0,"area":"deeprl"},{"id":"2607.20822","title":"Robust Asynchronous Q-Learning under Reward and State Corruption via Batching","created":"2026-07-23","cited_by_count":0,"area":"deeprl"},{"id":"2607.21876","title":"Variance-Reduced Q-Learning over Static and Time-Varying Networks","created":"2026-07-24","cited_by_count":0,"area":"deeprl"},{"id":"2610.06918","title":"Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning","created":"2026-10-02","cited_by_count":0,"area":"deeprl"},{"id":"2610.13901","title":"Hitting-Time Bounds for Asynchronous Q-Learning","created":"2026-10-09","cited_by_count":0,"area":"deeprl"},{"id":"2610.18839","title":"Fast Rates for Offline Q-Learning with KL Regularization","created":"2026-10-13","cited_by_count":0,"area":"deeprl"}],"next_cursor":null}