Non-Stationary RL

RL: Reinforcement Learning

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring
May 4, 2026Deyi Ji, Junyu Lu, Xuanyi Liu +7Non-Stationary RL Online Advertising
Tencent · Dalian University of Technology · Peking University +2
Data Deletion Can Help in Adaptive RL
Apr 30, 2026Param Budhraja, Aditya Gangrade, Alex Olshevsky +1Non-Stationary RL Reinforcement Learning
Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA · Faculty of Electrical and Computer Engineering, Systems Engineering, Boston University, Boston, MA 02215, USA
Safe Continual Reinforcement Learning in Non-stationary Environments
Apr 21, 2026Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro +1RL Control Non-Stationary RL
Institute for Software Integrated Systems, Vanderbilt University
DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
Apr 17, 2026Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. VeeravalliNon-Stationary RL Regret Minimization in RL
ECE and CSL, The Grainger College of Engineering University of Illinois at Urbana-Champaign
The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning
Apr 17, 2026Deep Kumar Ganguly, Chandradithya S Jonnalagadda, Pratham Chintamani +1Cooperative Game Theory Multi-Agent Coordination
Technical University of Munich (TU Munich) · Brown University · Indian Institute of Technology Tirupati (IIT Tirupati)
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Apr 16, 2026Lute Lillo, Nick CheneyNon-Stationary RL Reinforcement Learning
Department of Computer Science University of Vermont USA
Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
Mar 6, 2026Dane MalenfantNon-Stationary RL Decentralized MARL
School of Computer Science McGill University Mila - The Québec AI Institute
Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
Jan 27, 2026Tongxi Wang, Zhuoyang Xia, Xinran Chen +1Non-Stationary RL Sequential Decision Making
School of Future Technology, Southeast University, Nanjing, China · School of Automation, Southeast University, Nanjing, China
Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
Jan 21, 2026Asim H. Gazi, Yongyi Guo, Daiqi Gao +3Non-Stationary RL Reinforcement Learning
Department of Computer Science, Harvard University · Department of Statistics, Harvard University · Department of Statistics, University of Wisconsin–Madison +2
Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
May 2, 2025Zhiqiang He, Zhi LiuNon-Stationary RL Neural Network Training Dynamics
Department of Computer and Network Engineering, the University of Electro-Communications, Japan.
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
Mar 24, 2025Mohsen Amiri, Sindri MagnússonNon-Stationary RL Markov Decision Processes
Department of Computer and System Science, Stockholm University, SE-164 25 Stockholm, Sweden
Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion
Aug 19, 2024Achref Jaziri, Etienne Künzel, Visvanathan RameshStability-Plasticity Dilemma Non-Stationary RL
Department of Computer Science and Mathematics, Goethe University · HessianAI, Frankfurt am Main, Germany

May 4, 2026, cs.CLJ/K move · Enter open · S save

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring

Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu+6

Tencent · Dalian University of Technology · Peking University+2

Non-Stationary RL Online Advertising LLM-Guided RL

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Online advertising governance faces significant challenges due to the non-stationary nature of regulatory policies, where emerging mandates (e.g., restrictions on education or aesthetic anxiety) create severe label inconsistencies and reasoning ambiguities in historical datasets. In this paper, we propose ARGUS, a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. ARGUS addresses the sparsity of new policy data by employing a three-stage framework: (1) Policy Seeding for initial perception; (2) Adversarial Label Rectification, which utilizes a

Prosecutor-Defender-Umpire'' architecture to resolve conflicts between stale labels and new mandates; and (3) Latent Knowledge Discovery, which employs a tripartite dialectical discussion to unearth sophisticated,

gray-area'' violations. By leveraging RAG-enhanced policy knowledge and Chain-of-Thought synthesis as dynamic rewards for reinforcement learning, ARGUS synchronizes its reasoning pathways with evolving regulations. Extensive experiments on both industrial and public datasets demonstrate that ARGUS significantly outperforms traditional fine-tuning baselines, achieving superior policy-adaptive learning with minimal gold data.

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Latest papers 42