{"total":24,"rows":[{"id":"2205.10330","title":"A Review of Safe Reinforcement Learning: Methods, Theory and Applications","created":"2022-05-20","cited_by_count":181,"area":null},{"id":"2202.06558","title":"Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation","created":"2022-02-14","cited_by_count":69,"area":null},{"id":"2306.11700","title":"Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs","created":"2023-06-20","cited_by_count":28,"area":null},{"id":"2105.14937","title":"Safe Pontryagin Differentiable Programming","created":"2021-05-31","cited_by_count":26,"area":null},{"id":"2207.02242","title":"State-Augmented Learnable Algorithms for Resource Management in Wireless Networks","created":"2022-07-05","cited_by_count":14,"area":null},{"id":"2306.17279","title":"Probabilistic Constraint for Safety-Critical Reinforcement Learning","created":"2023-06-29","cited_by_count":11,"area":null},{"id":"2312.00561","title":"A safe exploration approach to constrained Markov decision processes","created":"2023-12-01","cited_by_count":10,"area":null},{"id":"2302.00808","title":"ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints","created":"2023-02-02","cited_by_count":4,"area":null},{"id":"2212.01505","title":"Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics","created":"2022-12-03","cited_by_count":3,"area":null},{"id":"2406.01782","title":"Multi-agent assignment via state augmented reinforcement learning","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2503.03736","title":"Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies","created":"2025-03-05","cited_by_count":3,"area":null},{"id":"2506.18748","title":"Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression","created":"2025-06-23","cited_by_count":3,"area":null},{"id":"2405.05748","title":"Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2506.02203","title":"Constrained Sliced Wasserstein Embedding","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2310.00248","title":"Learning State-Augmented Policies for Information Routing in Communication Networks","created":"2023-09-30","cited_by_count":1,"area":null},{"id":"2312.17194","title":"Resilient Constrained Reinforcement Learning","created":"2023-12-28","cited_by_count":1,"area":null},{"id":"2605.30461","title":"Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2210.16412","title":"A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks","created":"2022-10-28","cited_by_count":0,"area":null},{"id":"2311.00880","title":"SCPO: Safe Reinforcement Learning with Safety Critic Policy Optimization","created":"2023-11-01","cited_by_count":0,"area":null},{"id":"2512.15824","title":"State-Augmented Graphs for Circular Economy Triage","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2605.06227","title":"Price of Fairness in Short-Term and Long-Term Algorithmic Selections","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.02337","title":"Coordination Graphs for Constrained Multi-Agent Reinforcement Learning","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.03794","title":"Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2609.36478","title":"Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}