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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AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning
Sep 28, 2026Yingbo Zhao, Zeyu Yang, Zhoufan ZhuMulti-Objective Reinforcement Learning Non-Stationary RL
School of Economics Xiamen University Fujian, China · Paula and Gregory Chow Institute for Studies in Economics Xiamen University Fujian, China · School of Economics & Wang Yanan Institute for Studies in Economics Xiamen University Fujian, China
Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
Sep 16, 2026Everest Yang, Skye Thompson, George D. KonidarisNon-Stationary RL Continual Robot Learning
Brown University, Providence, RI, USA
Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning
Sep 8, 2026Tim TomashevskiyNon-Stationary RL Constrained RL
Department of Computing and Software McMaster University Hamilton, Ontario, Canada
ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning
Aug 5, 2026R. Blake Lawlor, Daniel S. BrownNon-Stationary RL Reinforcement Learning
1Kahlert School of Computing, University of Utah
A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics
Jul 28, 2026Zheshun Wu, Renjie Zheng, Jinhang Zuo +2Non-Stationary RL Regret Minimization in RL
Southern University of Science and Technology · Harbin Institute of Technology, Shenzhen Shenzhen, Guangdong, China · Southern University of Science and Technology Shenzhen, Guangdong, China +4
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Jul 27, 2026Luc McCutcheon, Evangelos Chatzaroulas, Saber FallahNon-Stationary RL Neural Network Initialization
Department of Mechanical Engineering, University of Surrey, United Kingdom · Department of Computing Science, University of Surrey, United Kingdom
Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations
Jul 26, 2026Young Hyun Cho, Franz Stoll, Will Wei Sun +2Non-Stationary RL Hierarchical RL
Department of Statistics, Harvard University · Edwardson School of Industrial Engineering, Purdue University · Mitch Daniels School of Business, Purdue University +3
Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
Jul 21, 2026Timothy TomashevskiyNon-Stationary RL Safe RL
Department of Computing and Software McMaster University Hamilton, Ontario, Canada
Breaking Feedback-Blindness: Utility-Augmented Transformer for Sequential Decision Making
Jul 21, 2026Yuyang Shen, Shan Dai, Daimin ChenNon-Stationary RL Sequential Decision Making
The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data · University of International Business and Economics
PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks
Jul 20, 2026Wen Qiu, Zhiqiang He, Wei Zhao +1Non-Stationary RL Reinforcement Learning
Department of Information and Communication Engineering, Kitami Institute of Technology, Japan · Graduate School of Informatics and Engineering, University of Electro-Communications, Japan · School of Computer Science and Technology, Anhui University of Technology, China
In-Context Reinforcement Learning under Non-Stationarity: A Survey
Jul 1, 2026A Run, Ziluo DingNon-Stationary RL In-Context RL
Independent Researcher · Beijing Innovation Center of Humanoid Robotics
Priced Motion Through Optimal Faces: A Normal-Fan Geometry for Non-Stationary Adversarial MDPs
Jun 27, 2026Kai HidajatNon-Stationary RL Markov Decision Processes
Komori Research
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Jun 22, 2026Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7RL Control Non-Stationary RL
University of Chicago · University of Michigan, Ann Arbor · Fermi National Accelerator Laboratory
Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
Jun 16, 2026Yanwei Cui, Xing Zhang, Yulong Zhang +4Continual Learning for LLM Agents Non-Stationary RL
AWS Generative AI Innovation Center · Amazon Web Services (AWS) · BingX Group Limited.
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Jun 15, 2026Negin Musavi, Gokul Puthumanaillam, Ruben Hernandez +2Non-Stationary RL Robot Policy Adaptation
Department of Aerospace Engineering, University of Illinois Urbana–Champaign, Champaign, IL 61820, USA
A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning
Jun 15, 2026Ardianto Wibowo, Paulo E Santos, Amer Baghdadi +3Distribution Shift Robustness Non-Stationary RL
IMT Atlantique, Brest, France · Flinders University, Adelaide, Australia · IRL Crossing, Adelaide, Australia +1
Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Deep Reinforcement Learning
Jun 10, 2026Felix Störck, Fabian Hinder, Barbara HammerNon-Stationary RL Reinforcement Learning
CITEC, Faculty of Technology, Bielefeld University, Germany.
GUI-AC: Enhancing Continual Learning in GUI Agents
Jun 9, 2026Can Lin, Tao Feng, Hangjie Yuan +3Non-Stationary RL RL for GUI Agents
Beijing University of Posts and Telecommunications · Tsinghua University · Zhejiang University +1
Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions
Jun 2, 2026Bingxu Liu, Jiashun Liu, Johan Obando-Ceron +5Non-Stationary RL Reinforcement Learning
Hong Kong University of Science and Technology · Fudan University · Mila - Québec AI Institute +2
Position: Deployed Reinforcement Learning should be Continual
Jun 1, 2026Parnian Behdin, Kevin Roice, Golnaz MesbahiNon-Stationary RL Reinforcement Learning
Alberta Machine Intelligence Institute (Amii), Edmonton, Canada.
Balancing Plasticity and Stability with Fast and Slow Successor Features
May 25, 2026Raymond Chua, Doina Precup, Blake RichardsStability-Plasticity Dilemma Non-Stationary RL
School of Computer Science, McGill University, Montr´eal, Canada · Mila - Quebec Artificial Intelligence Institute, Montr´eal, QC, Canada · CIFAR Learning in Machines and Brains +3
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
May 24, 2026Kordel K. France, Ovidiu DaescuNon-Stationary RL Partially Observable RL
Department of Computer Science, University of Texas at Dallas, USA
A perspective on fluid mechanical environments for challenges in reinforcement learning
May 24, 2026Shruti Mishra, Michael Chang, Vamsi Spandan +1RL Control Non-Stationary RL
Sony AI · Cohere Labs Community · The Hebrew University of Jerusalem
Scalable Reinforcement Learning via Adaptive Batch Scaling
May 20, 2026Jongchan ParkNon-Stationary RL Reinforcement Learning
Hyundai Motor Company
BAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control
May 15, 2026Yifan Zhang, Liang ZhengRL Control Non-Stationary RL
Central South University
ALSO: Adversarial Online Strategy Optimization for Social Agents
May 15, 2026Xiang Li, Liping Yi, Mingze Kong +3Non-Stationary RL Large Language Model-Based Social Simulation
School of Artificial Intelligence, Tianjin University, Tianjin, China · The Chinese University of Hong Kong, Shenzhen, China · East China Normal University, Shanghai, China.
Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
May 13, 2026Timofey TomashevskiyNon-Stationary RL Reinforcement Learning
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
From Cumulative Constraints to Adaptive Runtime Safety Control for Nonstationary Reinforcement Learning
May 13, 2026Timofey TomashevskiyNon-Stationary RL Constrained RL
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
A Harmonic Mean Formulation of Average Reward Reinforcement Learning in SMDPs
May 6, 2026Erel Shtossel, Alicia Vidler, Uri Shaham +1Non-Stationary RL Reinforcement Learning
Bar Ilan University Ramat Gan, Israel
Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
May 5, 2026Riddhiman Bhattacharyya, Sayak Chakrabarty, Imon BanerjeeNon-Stationary RL Markov Decision Processes
Department of Statistics, University of California, Santa Cruz Santa Cruz, California, USA · Department of Computer Science Northwestern University Evanston, IL, USA · Department of Industrial Engineering and Management Sciences Northwestern University Evanston, IL, USA

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AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning

Yingbo Zhao, Zeyu Yang, Zhoufan Zhu

School of Economics Xiamen University Fujian, China · Paula and Gregory Chow Institute for Studies in Economics Xiamen University Fujian, China · School of Economics & Wang Yanan Institute for Studies in Economics Xiamen University Fujian, China

Multi-Objective Reinforcement Learning Non-Stationary RL Quantitative Finance Automated Algorithm Discovery

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Formulaic alpha discovery is a core challenge in quantitative trading, as identifying alphas that work well together remains difficult. Recent reinforcement learning (RL) methods formulate this task as a Markov decision process (MDP), but two important issues remain unresolved. First, as the alpha pool evolves, the reward function changes accordingly, making the MDP inherently non-stationary. Second, most existing methods optimize a single objective, typically predictive power, while ignoring other important properties of a high-quality alpha pool. Motivated by these challenges, we propose AlphaPareto, an RL method for formulaic alpha discovery. To address non-stationarity, AlphaPareto augments the state to include both the alpha under construction and the current alpha pool, and applies a large language model (LLM) to encode the pool. This design allows the agent to adapt to the evolving search environment. To overcome the limitation of single-objective reward design, AlphaPareto replaces the scalar reward with a multi-objective vector-valued reward that simultaneously captures predictive power, temporal stability, perturbation robustness, and diversity, and optimizes these objectives through a Pareto-regularized learning procedure. Empirical applications to real-world datasets show that our AlphaPareto method outperforms its competitors.

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