cluster #9

Papers

• A Tour Of Reinforcement Learning: The View From Continuous Control (2018)
  Benjamin Recht
  19.86
• Reinforcement Learning In Economics And Finance (2020)
  Arthur Charpentier, Romuald Elie, Carl Remlinger
  14.73
• Deep Hierarchical Reinforcement Learning Algorithm In Partially Observable Markov Decision Processes (2018)
  Le Pham Tuyen, Ngo Anh Vien, Abu Layek, et al.
  12.87
• Provably Efficient Reinforcement Learning With Linear Function Approximation (2019)
  Chi Jin, Zhuoran Yang, Zhaoran Wang, et al.
  11.76
• Sample Complexity Of Asynchronous Q-learning: Sharper Analysis And Variance Reduction (2020)
  Gen Li, Yuting Wei, Yuejie Chi, et al.
  11.19
• Robust Reinforcement Learning: A Case Study In Linear Quadratic Regulation (2020)
  Bo Pang, Zhong-Ping Jiang
  11.19
• Soft Policy Gradient Method For Maximum Entropy Deep Reinforcement Learning (2019)
  Wenjie Shi, Shiji Song, Cheng Wu
  10.85
• Direct And Indirect Reinforcement Learning (2019)
  Yang Guan, Shengbo Eben Li, Jingliang Duan, et al.
  10.74
• Achieving Zero Constraint Violation For Constrained Reinforcement Learning Via Primal-dual Approach (2021)
  Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, et al.
  9.59
• Modeling The Effects Of Environmental And Perceptual Uncertainty Using Deterministic Reinforcement Learning Dynamics With Partial Observability (2021)
  Wolfram Barfuss, Richard P. Mann
  9.59
• On Overfitting And Asymptotic Bias In Batch Reinforcement Learning With Partial Observability (2017)
  Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, et al.
  9.23
• Convergence Of Policy Gradient Methods For Finite-horizon Exploratory Linear-quadratic Control Problems (2022)
  Michael Giegrich, Christoph Reisinger, Yufei Zhang
  9.23
• Convergence Guarantees Of Policy Optimization Methods For Markovian Jump Linear Systems (2020)
  Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
  9.03
• Breaking The Sample Size Barrier In Model-based Reinforcement Learning With A Generative Model (2020)
  Gen Li, Yuting Wei, Yuejie Chi, et al.
  9.03
• Regret Bounds For Reinforcement Learning Via Markov Chain Concentration (2018)
  Ronald Ortner
  9.03
• Learning And Information In Stochastic Networks And Queues (2021)
  Neil Walton, Kuang Xu
  9.03
• Rethinking The Discount Factor In Reinforcement Learning: A Decision Theoretic Approach (2019)
  Silviu Pitis
  8.60
• Parameterized Mdps And Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework (2020)
  Amber Srivastava, Srinivasa M Salapaka
  8.60
• Revisiting LQR Control From The Perspective Of Receding-horizon Policy Gradient (2023)
  Xiangyuan Zhang, Tamer Başar
  8.60
• Q-learning Lagrange Policies For Multi-action Restless Bandits (2021)
  Jackson A. Killian, Arpita Biswas, Sanket Shah, et al.
  8.35
• Unified Models Of Human Behavioral Agents In Bandits, Contextual Bandits And RL (2020)
  Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, et al.
  8.35
• Minimax Optimal Q Learning With Nearest Neighbors (2023)
  Puning Zhao, Lifeng Lai
  8.09
• Logarithmic Regret For Episodic Continuous-time Linear-quadratic Reinforcement Learning Over A Finite-time Horizon (2020)
  Matteo Basei, Xin Guo, Anran Hu, et al.
  7.81
• DSAC: Distributional Soft Actor-critic For Risk-sensitive Reinforcement Learning (2020)
  Xiaoteng Ma, Junyao Chen, Li Xia, et al.
  7.81
• Computably Continuous Reinforcement-learning Objectives Are Pac-learnable (2023)
  Cambridge Yang, Michael Littman, Michael Carbin
  7.81
• Approximating Euclidean By Imprecise Markov Decision Processes (2020)
  Manfred Jaeger, Giorgio Bacci, Giovanni Bacci, et al.
  7.50
• Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning (2018)
  Jayakumar Subramanian, Aditya Mahajan
  7.50
• Effective Multi-user Delay-constrained Scheduling With Deep Recurrent Reinforcement Learning (2022)
  Pihe Hu, Ling Pan, Yu Chen, et al.
  7.16
• An Online Prediction Algorithm For Reinforcement Learning With Linear Function Approximation Using Cross Entropy Method (2018)
  Ajin George Joseph, Shalabh Bhatnagar
  7.16
• Entropic Regularization Of Markov Decision Processes (2019)
  Boris Belousov, Jan Peters
  6.77
• Reinforcement Learning In Pomdps With Memoryless Options And Option-observation Initiation Sets (2017)
  Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, et al.
  6.77
• Efficient Learning In Non-stationary Linear Markov Decision Processes (2020)
  Ahmed Touati, Pascal Vincent
  6.77
• Entropy Regularized Reinforcement Learning Using Large Deviation Theory (2021)
  Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, et al.
  6.34
• Optimality-based Analysis Of XCSF Compaction In Discrete Reinforcement Learning (2020)
  Jordan T. Bishop, Marcus Gallagher
  6.34
• Multi-timescale Ensemble Q-learning For Markov Decision Process Policy Optimization (2024)
  Talha Bozkus, Urbashi Mitra
  6.34
• Linear Convergence Of Entropy-regularized Natural Policy Gradient With Linear Function Approximation (2021)
  Semih Cayci, Niao He, R. Srikant
  6.34
• Learning In Restless Bandits Under Exogenous Global Markov Process (2021)
  Tomer Gafni, Michal Yemini, Kobi Cohen
  6.34
• Softmax Policy Gradient Methods Can Take Exponential Time To Converge (2021)
  Gen Li, Yuting Wei, Yuejie Chi, et al.
  6.34
• An Efficient Off-policy Reinforcement Learning Algorithm For The Continuous-time LQR Problem (2023)
  Victor G. Lopez, Matthias A. Müller
  6.34
• Reinforcement Learning Under Partial Observability Guided By Learned Environment Models (2022)
  Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, et al.
  6.34
• Anomaly Detection Via Learning-based Sequential Controlled Sensing (2023)
  Geethu Joseph, Chen Zhong, M. Cenk Gursoy, et al.
  5.84
• Performance Dynamics And Termination Errors In Reinforcement Learning: A Unifying Perspective (2019)
  Nikki Lijing Kuang, Clement H. C. Leung
  5.84
• Sparse Tree Search Optimality Guarantees In Pomdps With Continuous Observation Spaces (2019)
  Michael H. Lim, Claire J. Tomlin, Zachary N. Sunberg
  5.84
• Active Inference And Reinforcement Learning: A Unified Inference On Continuous State And Action Spaces Under Partial Observability (2022)
  Parvin Malekzadeh, Konstantinos N. Plataniotis
  5.84
• Robust Risk-sensitive Reinforcement Learning With Conditional Value-at-risk (2024)
  Xinyi Ni, Lifeng Lai
  5.84
• Modelling Stock-market Investors As Reinforcement Learning Agents [correction] (2016)
  Alvin Pastore, Umberto Esposito, Eleni Vasilaki
  5.84
• Certrl: Formalizing Convergence Proofs For Value And Policy Iteration In Coq (2020)
  Koundinya Vajjha, Avraham Shinnar, Vasily Pestun, et al.
  5.84
• Hidden Markov Model Estimation-based Q-learning For Partially Observable Markov Decision Process (2018)
  Hyung-Jin Yoon, Donghwan Lee, Naira Hovakimyan
  5.84
• Reinforcement Learning With Non-cumulative Objective (2023)
  Wei Cui, Wei Yu
  5.24
• Replicable Reinforcement Learning With Linear Function Approximation (2025)
  Eric Eaton, Marcel Hussing, Michael Kearns, et al.
  5.24