Model-Free RL

RL: Reinforcement Learning

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

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Latest papers 45

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RLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms
Oct 6, 2026Haoran Li, Zengle Ge, Xiaomin Yuan +14Automated Algorithm Discovery Model-Free RL
University of the Chinese Academy of Sciences · Baidu · Tsinghua University +4
Reachability-Aware Diffusion Policy Optimization
Oct 5, 2026Hikmet Simsir, Kutay Demiray, Ozgur S. OguzConstrained RL Diffusion Policy
Department of Computer Engineering, Bilkent University
Taylor Representations for Model-Free RL in Networked MDPs
Oct 4, 2026Salah Chikhi, Abdelhaq Chaoui, Asuman Ozdaglar +1Markov Decision Processes Representation Learning for RL
Massachusetts Institute of Technology
Q-Learning for Reachability in MEC-Free MDPs
Oct 1, 2026Lu-Chin Chang, Suguman BansalMarkov Decision Processes Reinforcement Learning
Georgia Institute of Technology
Deep Epistemic Value Functions for Optimistic Exploration
Sep 28, 2026Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1Value Function Estimation RL Exploration
ETH Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Germany
GraphHCA: Closed-Form Hindsight Credit Assignment for Long-Horizon LLM Agents
Sep 28, 2026Haodong Zhu, Yangyang Ren, Changbai Li +4Credit Assignment in RL Step-Level Credit Assignment in RL
Beihang University · Zhongguancun Academy · Communication University of China +1
Lifted Bellman Linear Programming for Offline Reinforcement Learning
Sep 21, 2026Hyukjun Yang, Jongchan Park, Narim Jeong +1Reinforcement Learning Offline RL
Korea Advanced Institute of Science and Technology(KAIST) · NAVER LABS
ProDVI: Programmatic Dynamics Priors for Value Network Initialization
Aug 6, 2026Xinwei Liu, Junyuan Liang, Jianting Zhang +1Reinforcement Learning LLM-Guided RL
1Sun Yat-sen University · 2Purdue University
Observation-Grounded Self-Predictive Reinforcement Learning for Visual Continuous Control
Aug 6, 2026Xinwei Liu, Junyuan Liang, Jianting Zhang +1Predictive Representation Learning Continuous Control
Sun Yat-sen University · Purdue University
Exact Model-Free Policy Iteration for Co-safe LTL Planning
Aug 5, 2026Zetong Xuan, Yu WangReinforcement Learning Policy Iteration
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611 USA
Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning
Aug 3, 2026Ankur Naskar, Vivek T A, Aditya Kumar +2Reinforcement Learning Risk-Sensitive RL
Indian Institute of Science, Bengaluru, India · Indian Institute of Technology Madras, Chennai, India
Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control
Jul 30, 2026Takumi Shioda, Kohei Terashima, Tatsuo NagaiLanguage Model-Based Control Model-Free RL
The University of Tokyo · Tokyo University of Science
Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
Jul 28, 2026Ya-Chia Shen, Woei-Leong ChanRL Control Quadrotor Control
Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Aeronautics and Astronautics, National Cheng Kung University
Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States
Jul 20, 2026Armin SommerReinforcement Learning Model-Free RL
ETH Zurich, Switzerland
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
Jul 18, 2026Alireza Furutanpey, Schahram DustdarFree Energy Calculation Intrinsic Reward Methods
Coovally, Barcelona, Spain · Distributed Systems Group, TU Vienna, Austria · ICREA Barcelona, Spain
Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
Jul 13, 2026Ziheng Cheng, Xin Guo, Huyên Pham +1Policy Gradient Continuous-Time RL
University of California, Berkeley · Ecole Polytechnique, CMAP · Imperial College London
Can Reinforcement Learning Efficiently Discover Price Manipulation?
Jul 7, 2026Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio LilloModel-Free RL Algorithmic Trading
Scuola Normale Superiore, Piazza dei Cavalieri 7, Pisa 56126, Italy.
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Jun 25, 2026Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1Reinforcement Learning Stochastic Optimal Control
Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. · Department of Statistics and Data Science, University of California, Los Angeles, CA, USA. · Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
Jun 23, 2026Samer Nasser, Henrique Duarte Moura, Ritesh Kumar Singh +2Internet of Things Model-Free RL
University of Antwerp - imec, IDLab Research Group, Antwerp, Belgium
NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
Jun 19, 2026Xinwei Liu, Junyuan Liang, Zicong Hong +2Reinforcement Learning Q-Learning
Sun Yat-sen University, China · EPFL, Switzerland · Purdue University, USA
Inverting the Bellman Equation: From $Q$ -Values to World Models
Jun 19, 2026Alistair Letcher, Mattie Fellows, Alexander D. Goldie +3Reinforcement Learning Model-Free RL
FLAIR, University of Oxford · Google DeepMind · Mila, University of Montreal
Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems
Jun 17, 2026Hugo O. Garcés, Alejandro J. Rojas, Bernardo A. Hernández +5RL Control Cyber-Physical Systems
Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Concepción, Concepción, Chile · Electrical Engineering Departament, Universidad de Talca · Department of Electrical & Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Alberta, AB, Canada +2
Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process
Jun 15, 2026Jongmin Lee, Ernest K. Ryu, Vaneet AggarwalMarkov Decision Processes Reinforcement Learning
Seoul National University · UCLA · Purdue University
Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems
Jun 10, 2026Xin Guo, Yijie Huang, Xiang YuPolicy Gradient Stochastic Optimal Control
Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA. · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
Jun 9, 2026Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio +2RL Control Multi-Objective Reinforcement Learning
Politecnico di Milano · STMicroelectronics
An Agency-Transferring Model-Free Policy Enhancement Technique
Jun 8, 2026Anton Bolychev, Georgiy Malaniya, Sinan Ibrahim +1RL Control Transfer Learning
Center for Engineering Systems and Sciences · Central University; Center for Engineering Systems and Sciences; Sirius University of Science and Technology
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Jun 4, 2026Johan Obando-Ceron, Lu Li, Scott Fujimoto +3Reinforcement Learning Representation Learning for RL
Mila – Québec AI Institute · Université de Montréal · McGill University +2
Cortex and subcortex play distinct roles over learning when cortical memory is limited
May 30, 2026Matthew Farrell, Taro ToyoizumiComputational Cognitive Modeling Reinforcement Learning
Laboratory for Neural Computation and Adaptation RIKEN Center for Brain Science, Wako, Japan · Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
ReversedQ: Opportunities for Faster Q-Learning in Episodic Online Reinforcement Learning
May 20, 2026Sofia R. Miskala-Dinc, Aviva PrinsReinforcement Learning Q-Learning
University of Maryland College Park, MD, United States
Peng's Q( $λ$ ) for Conservative Value Estimation in Offline Reinforcement Learning
May 14, 2026Byeongchan Kim, Min-hwan OhReinforcement Learning Value Function Estimation
Seoul National University

Oct 6, 2026, cs.AIJ/K move · Enter open · S save

RLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms

Haoran Li, Zengle Ge, Xiaomin Yuan, Yui Lo+13

University of the Chinese Academy of Sciences · Baidu · Tsinghua University+4

Automated Algorithm Discovery Model-Free RL Self-Evolving Agents Large Language Model-Guided Optimization Genetic Programming

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LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self-evolve to improve how agents learn. However, realizing this prospect faces two obstacles. Joint search over coupled algorithmic components is difficult to scale: simultaneous changes can disrupt learning, while isolated changes overlook their dependencies. Evaluating candidate algorithms also requires costly training, with fitness remaining uncertain across random seeds. We introduce RLDiscover, a framework for the self-evolution of model-free deep RL algorithms. Progressive Co-Evolution advances from targeted component edits to joint evolution, while Progressive Probabilistic Evaluation balances search breadth and evaluation fidelity through staged training and repeated evaluation. Experiments across SAC, PPO, and DQN on four benchmark suites show substantial improvements in mean return, with per-family median gains of 32%-84% and a peak return ratio of approximately 363x over a near-zero baseline. These gains include transitions from failed learning to successful task completion, and improvements persist when evolution starts from stronger open-source implementations. On measured SAC locomotion runs, evaluation uses approximately one-fifteenth the estimated compute required to fully evaluate the same candidate pool. Remarkably, independent searches repeatedly discover interpretable combinations of adaptive robust losses, progress-dependent value targets, and running statistics, with selected programs transferring to unseen tasks. These findings point toward a broader role for self-evolution in AI: discovering interpretable algorithms that improve how agents learn.

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Latest papers 45