{"total":65,"rows":[{"id":"2109.11251","title":"Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning","created":"2021-09-23","cited_by_count":186,"area":null},{"id":"2305.16589","title":"The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model","created":"2023-05-26","cited_by_count":52,"area":null},{"id":"2108.13035","title":"SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning","created":"2021-08-30","cited_by_count":31,"area":null},{"id":"2309.11359","title":"Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning","created":"2023-09-20","cited_by_count":31,"area":"offlinerl"},{"id":"2307.07907","title":"Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation","created":"2023-07-15","cited_by_count":25,"area":null},{"id":"2203.12759","title":"Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots","created":"2022-03-23","cited_by_count":22,"area":null},{"id":"2110.03375","title":"Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning","created":"2021-10-07","cited_by_count":18,"area":null},{"id":"2411.15370","title":"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers","created":"2024-11-22","cited_by_count":18,"area":"deeprl"},{"id":"2202.12180","title":"Quantum Deep Reinforcement Learning for Robot Navigation Tasks","created":"2022-02-24","cited_by_count":16,"area":null},{"id":"2210.15479","title":"Low-Rank Modular Reinforcement Learning via Muscle Synergy","created":"2022-10-26","cited_by_count":16,"area":null},{"id":"2102.11075","title":"SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning","created":"2021-02-22","cited_by_count":15,"area":null},{"id":"2511.04898","title":"Real-Time Reasoning Agents in Evolving Environments","created":"2025-11-07","cited_by_count":15,"area":"lr"},{"id":"2210.02317","title":"Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers","created":"2022-10-05","cited_by_count":13,"area":null},{"id":"2403.12309","title":"Reinforcement Learning from Delayed Observations via World Models","created":"2024-03-18","cited_by_count":13,"area":"wm"},{"id":"2407.00324","title":"Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning","created":"2024-06-29","cited_by_count":13,"area":"deeprl"},{"id":"2502.19652","title":"Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning","created":"2025-02-27","cited_by_count":13,"area":"deeprl"},{"id":"2303.14716","title":"Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning","created":"2023-03-26","cited_by_count":12,"area":"offlinerl"},{"id":"2311.02129","title":"Hierarchical Reinforcement Learning for Power Network Topology Control","created":"2023-11-03","cited_by_count":11,"area":null},{"id":"2312.15339","title":"MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning","created":"2023-12-23","cited_by_count":10,"area":null},{"id":"2103.05147","title":"Model-free Policy Learning with Reward Gradients","created":"2021-03-09","cited_by_count":9,"area":null},{"id":"2112.11622","title":"An Alternate Policy Gradient Estimator for Softmax Policies","created":"2021-12-22","cited_by_count":9,"area":null},{"id":"2102.04881","title":"Measuring Progress in Deep Reinforcement Learning Sample Efficiency","created":"2021-02-09","cited_by_count":8,"area":null},{"id":"2310.09426","title":"Offline Reinforcement Learning for Optimizing Production Bidding Policies","created":"2023-10-13","cited_by_count":8,"area":"offlinerl"},{"id":"2110.00704","title":"OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation","created":"2021-10-02","cited_by_count":7,"area":null},{"id":"2410.16632","title":"Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies","created":"2024-10-22","cited_by_count":7,"area":"deeprl"},{"id":"2202.11296","title":"Reinforcement Learning in Practice: Opportunities and Challenges","created":"2022-02-23","cited_by_count":6,"area":null},{"id":"2205.08716","title":"No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL","created":"2022-05-18","cited_by_count":6,"area":null},{"id":"2406.03276","title":"Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning","created":"2024-06-05","cited_by_count":5,"area":"deeprl"},{"id":"2511.09737","title":"Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy","created":"2025-11-12","cited_by_count":5,"area":null},{"id":"2607.01111","title":"FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement","created":"2026-07-01","cited_by_count":5,"area":null},{"id":"2305.05760","title":"Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization","created":"2023-05-09","cited_by_count":4,"area":null},{"id":"2102.02639","title":"Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym","created":"2021-02-02","cited_by_count":3,"area":null},{"id":"2201.05599","title":"Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning","created":"2022-01-14","cited_by_count":3,"area":null},{"id":"2309.16718","title":"A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning","created":"2023-09-13","cited_by_count":3,"area":"offlinerl"},{"id":"2405.16195","title":"Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning","created":"2024-05-25","cited_by_count":3,"area":"deeprl"},{"id":"2411.11088","title":"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces","created":"2024-11-17","cited_by_count":3,"area":"offlinerl"},{"id":"2110.08307","title":"GrowSpace: Learning How to Shape Plants","created":"2021-10-15","cited_by_count":2,"area":null},{"id":"2406.14951","title":"An Idiosyncrasy of Time-discretization in Reinforcement Learning","created":"2024-06-21","cited_by_count":2,"area":"deeprl"},{"id":"2410.14957","title":"Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations","created":"2024-10-19","cited_by_count":2,"area":"offlinerl"},{"id":"2107.06686","title":"Safer Reinforcement Learning through Transferable Instinct Networks","created":"2021-07-14","cited_by_count":1,"area":null},{"id":"2403.14425","title":"Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization","created":"2024-03-21","cited_by_count":1,"area":null},{"id":"2501.14443","title":"Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent","created":"2025-01-24","cited_by_count":1,"area":"deeprl"},{"id":"2503.05092","title":"Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation","created":"2025-03-07","cited_by_count":1,"area":"deeprl"},{"id":"2506.19997","title":"TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design","created":"2025-06-24","cited_by_count":1,"area":"deeprl"},{"id":"2606.18594","title":"Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation","created":"2026-06-17","cited_by_count":1,"area":"deeprl"},{"id":"2606.28433","title":"Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy","created":"2026-06-26","cited_by_count":1,"area":"deeprl"},{"id":"2106.02943","title":"Learning Routines for Effective Off-Policy Reinforcement Learning","created":"2021-06-05","cited_by_count":0,"area":null},{"id":"2110.06766","title":"Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification","created":"2021-10-13","cited_by_count":0,"area":null},{"id":"2201.04224","title":"Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics","created":"2022-01-11","cited_by_count":0,"area":null},{"id":"2203.13424","title":"Dealing with Sparse Rewards Using Graph Neural Networks","created":"2022-03-25","cited_by_count":0,"area":null},{"id":"2209.04999","title":"Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability","created":"2022-09-12","cited_by_count":0,"area":null},{"id":"2209.06604","title":"Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function","created":"2022-09-14","cited_by_count":0,"area":null},{"id":"2301.06987","title":"Sim-Anchored Learning for On-the-Fly Adaptation","created":"2023-01-17","cited_by_count":0,"area":null},{"id":"2310.05179","title":"DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaption","created":"2023-10-08","cited_by_count":0,"area":null},{"id":"2404.15410","title":"Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments","created":"2024-04-23","cited_by_count":0,"area":"deeprl"},{"id":"2406.18899","title":"Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning","created":"2024-06-27","cited_by_count":0,"area":null},{"id":"2507.19535","title":"Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks","created":"2025-07-22","cited_by_count":0,"area":"offlinerl"},{"id":"2509.08756","title":"Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform","created":"2025-09-10","cited_by_count":0,"area":null},{"id":"2509.19159","title":"Efficient Reinforcement Learning by Reducing Forgetting with Elephant Activation Functions","created":"2025-09-23","cited_by_count":0,"area":"deeprl"},{"id":"2510.06064","title":"Medical Vision Language Models as Policies for Robotic Surgery","created":"2025-10-07","cited_by_count":0,"area":"mllm"},{"id":"2511.09923","title":"Harnessing Bounded-Support Evolution Strategies for Policy Refinement","created":"2025-11-13","cited_by_count":0,"area":null},{"id":"2605.03125","title":"Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation","created":"2026-05-04","cited_by_count":0,"area":"deeprl"},{"id":"2607.15329","title":"Co-Design of Aeroelastic Systems with Deep Reinforcement Learning","created":"2026-07-16","cited_by_count":0,"area":"deeprl"},{"id":"2607.18488","title":"The Open Ant: A Robot Platform for Reinforcement Learning Research","created":"2026-07-20","cited_by_count":0,"area":"deeprl"},{"id":"2608.27996","title":"Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data","created":"2026-08-28","cited_by_count":0,"area":"wm"}],"next_cursor":null}