{"total":26,"rows":[{"id":"2407.18878","title":"A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach","created":"2024-07-26","cited_by_count":15,"area":"deeprl"},{"id":"2404.02108","title":"Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs","created":"2024-04-02","cited_by_count":14,"area":"deeprl"},{"id":"2402.02042","title":"Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm","created":"2024-02-03","cited_by_count":9,"area":"deeprl"},{"id":"2407.13743","title":"Optimistic Q-learning for average reward and episodic reinforcement learning","created":"2024-07-18","cited_by_count":9,"area":"deeprl"},{"id":"2505.15138","title":"Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm","created":"2025-05-21","cited_by_count":8,"area":"deeprl"},{"id":"2405.15050","title":"Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs","created":"2024-05-23","cited_by_count":7,"area":"deeprl"},{"id":"2502.16816","title":"Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning","created":"2025-02-24","cited_by_count":7,"area":"deeprl"},{"id":"2505.19986","title":"Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach","created":"2025-05-26","cited_by_count":7,"area":"deeprl"},{"id":"2410.23498","title":"Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm","created":"2024-10-30","cited_by_count":6,"area":"deeprl"},{"id":"2403.05738","title":"Provable Policy Gradient Methods for Average-Reward Markov Potential Games","created":"2024-03-09","cited_by_count":5,"area":"deeprl"},{"id":"2506.07040","title":"Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning","created":"2025-06-08","cited_by_count":4,"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":"2602.18003","title":"Policy Gradient Algorithms in Average-Reward Multichain MDPs","created":"2026-02-20","cited_by_count":3,"area":"deeprl"},{"id":"2410.19982","title":"Random Policy Enables In-Context Reinforcement Learning within Trust Horizons","created":"2024-10-25","cited_by_count":2,"area":"deeprl"},{"id":"2504.09035","title":"InterQ: A DQN Framework for Optimal Intermittent Control","created":"2025-04-12","cited_by_count":2,"area":"deeprl"},{"id":"2506.24005","title":"Provably Efficient and Agile Randomized Q-Learning","created":"2025-06-30","cited_by_count":2,"area":"deeprl"},{"id":"2605.11586","title":"Learning Weakly Communicating Average-Reward CMDPs: Strong Duality and Improved Regret","created":"2026-05-12","cited_by_count":2,"area":"deeprl"},{"id":"2507.22854","title":"A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model","created":"2025-07-30","cited_by_count":1,"area":"deeprl"},{"id":"2603.10895","title":"Ergodicity in reinforcement learning","created":"2026-03-11","cited_by_count":1,"area":"deeprl"},{"id":"2404.00686","title":"Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning","created":"2024-03-31","cited_by_count":0,"area":"deeprl"},{"id":"2505.15693","title":"Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives","created":"2025-05-21","cited_by_count":0,"area":"deeprl"},{"id":"2602.00781","title":"Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding","created":"2026-01-31","cited_by_count":0,"area":"deeprl"},{"id":"2603.04353","title":"A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications","created":"2026-03-04","cited_by_count":0,"area":"deeprl"},{"id":"2606.25012","title":"Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL","created":"2026-06-23","cited_by_count":0,"area":"deeprl"},{"id":"2608.07725","title":"Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning","created":"2026-08-07","cited_by_count":0,"area":"deeprl"},{"id":"2609.39093","title":"Learning Infinite-Horizon Average-Reward CMDPs via State Augmentation","created":"2026-09-30","cited_by_count":0,"area":"deeprl"}],"next_cursor":null}