{"total":20,"rows":[{"id":"2109.00157","title":"A Survey of Exploration Methods in Reinforcement Learning","created":"2021-09-01","cited_by_count":73,"area":null},{"id":"2201.03916","title":"Automated Reinforcement Learning (AutoRL): A Survey and Open Problems","created":"2022-01-11","cited_by_count":70,"area":null},{"id":"2201.12417","title":"Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error","created":"2022-01-28","cited_by_count":32,"area":null},{"id":"2205.07704","title":"From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses","created":"2022-05-16","cited_by_count":22,"area":null},{"id":"2203.12592","title":"Your Policy Regularizer is Secretly an Adversary","created":"2022-03-23","cited_by_count":21,"area":null},{"id":"2302.01275","title":"ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs","created":"2023-02-02","cited_by_count":20,"area":null},{"id":"2306.17750","title":"TD Convergence: An Optimization Perspective","created":"2023-06-30","cited_by_count":16,"area":null},{"id":"2210.12057","title":"Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization","created":"2022-10-21","cited_by_count":8,"area":null},{"id":"2307.02632","title":"Stability of Q-Learning Through Design and Optimism","created":"2023-07-05","cited_by_count":8,"area":null},{"id":"2106.15380","title":"Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes","created":"2021-06-29","cited_by_count":6,"area":null},{"id":"2309.05105","title":"Convex Q Learning in a Stochastic Environment: Extended Version","created":"2023-09-10","cited_by_count":5,"area":null},{"id":"2312.12869","title":"Parameterized Projected Bellman Operator","created":"2023-12-20","cited_by_count":3,"area":null},{"id":"2103.09756","title":"Near Optimal Policy Optimization via REPS","created":"2021-03-17","cited_by_count":1,"area":null},{"id":"2210.09409","title":"Sufficient Exploration for Convex Q-learning","created":"2022-10-17","cited_by_count":1,"area":null},{"id":"2509.19800","title":"Analysis of approximate linear programming solution to Markov decision problem with log barrier function","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2203.16177","title":"Marginalized Operators for Off-policy Reinforcement Learning","created":"2022-03-30","cited_by_count":0,"area":null},{"id":"2206.08499","title":"A Parametric Class of Approximate Gradient Updates for Policy Optimization","created":"2022-06-17","cited_by_count":0,"area":null},{"id":"2210.08131","title":"Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time","created":"2022-10-14","cited_by_count":0,"area":null},{"id":"2307.02345","title":"LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning","created":"2023-07-05","cited_by_count":0,"area":null},{"id":"2605.22507","title":"Generative Modeling by Value-Driven Transport","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":null}