Uncategorized

Papers

• Sigmoid-weighted Linear Units For Neural Network Function Approximation In Reinforcement Learning (2017)
  Stefan Elfwing, Eiji Uchibe, Kenji Doya
  24.15
• Transfer Learning In Deep Reinforcement Learning: A Survey (2020)
  Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain, et al.
  20.93
• A Tour Of Reinforcement Learning: The View From Continuous Control (2018)
  Benjamin Recht
  19.86
• Quantum-enhanced Machine Learning (2016)
  Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel
  19.33
• Distributional Reinforcement Learning With Quantile Regression (2017)
  Will Dabney, Mark Rowland, Marc G. Bellemare, et al.
  19.20
• Variational Quantum Circuits For Deep Reinforcement Learning (2019)
  Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, et al.
  19.19
• DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards (2026)
  Kaiyi Zhang et al.
  17.60
• State Representation Learning For Control: An Overview (2018)
  Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, et al.
  17.39
• Using Human Feedback To Fine-tune Diffusion Models Without Any Reward Model (2023)
  Kai Yang, Jian Tao, Jiafei Lyu, et al.
  17.39
• Tactics Of Adversarial Attack On Deep Reinforcement Learning Agents (2017)
  Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, et al.
  17.32
• Bridging Evolutionary Algorithms And Reinforcement Learning: A Comprehensive Survey On Hybrid Algorithms (2024)
  Pengyi Li, Jianye Hao, Hongyao Tang, et al.
  17.05
• Explainable Reinforcement Learning Through A Causal Lens (2019)
  Prashan Madumal, Tim Miller, Liz Sonenberg, et al.
  16.69
• Active Inference: Demystified And Compared (2019)
  Noor Sajid, Philip J. Ball, Thomas Parr, et al.
  15.98
• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)
  George A. Vouros
  15.80
• Shared Autonomy Via Deep Reinforcement Learning (2018)
  Siddharth Reddy, Anca D. Dragan, Sergey Levine
  15.40
• Alphastar: An Evolutionary Computation Perspective (2019)
  Kai Arulkumaran, Antoine Cully, Julian Togelius
  15.13
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey And Research Opportunities (2023)
  Yanjie Song, Yutong Wu, Yangyang Guo, et al.
  15.03
• Sophisticated Inference (2020)
  Karl Friston, Lancelot da Costa, Danijar Hafner, et al.
  14.83
• Interpretable Policies For Reinforcement Learning By Genetic Programming (2017)
  Daniel Hein, Steffen Udluft, Thomas A. Runkler
  14.76
• Reinforcement Learning Algorithms: An Overview And Classification (2022)
  Fadi Almahamid, Katarina Grolinger
  14.73
• Reinforcement Learning In Economics And Finance (2020)
  Arthur Charpentier, Romuald Elie, Carl Remlinger
  14.73
• Constrained Multi-objective Optimization With Deep Reinforcement Learning Assisted Operator Selection (2024)
  Fei Ming, Wenyin Gong, Ling Wang, et al.
  14.73
• Deep TAMER: Interactive Agent Shaping In High-dimensional State Spaces (2017)
  Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, et al.
  14.73
• Interestingness Elements For Explainable Reinforcement Learning: Understanding Agents' Capabilities And Limitations (2019)
  Pedro Sequeira, Melinda Gervasio
  14.55
• CODA: Rewriting Transformer Blocks as GEMM-Epilogue Programs (2026)
  Han Guo et al.
  14.31
• Intelligent Problem-solving As Integrated Hierarchical Reinforcement Learning (2022)
  Manfred Eppe, Christian Gumbsch, Matthias Kerzel, et al.
  14.02
• Real-time Model Calibration With Deep Reinforcement Learning (2020)
  Yuan Tian, Manuel Arias Chao, Chetan Kulkarni, et al.
  13.74
• Reinforcement Learning With Perturbed Rewards (2018)
  Jingkang Wang, Yang Liu, Bo Li
  13.74
• T-soft Update Of Target Network For Deep Reinforcement Learning (2020)
  Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo
  13.39
• Data-efficient Domain Randomization With Bayesian Optimization (2020)
  Fabio Muratore, Christian Eilers, Michael Gienger, et al.
  13.28
• Counterfactual State Explanations For Reinforcement Learning Agents Via Generative Deep Learning (2021)
  Matthew L. Olson, Roli Khanna, Lawrence Neal, et al.
  13.23
• Deep Reinforcement Learning, A Textbook (2022)
  Aske Plaat
  13.17
• Statistical Inference Of The Value Function For Reinforcement Learning In Infinite Horizon Settings (2020)
  C. Shi, S. Zhang, W. Lu, et al.
  13.14
• Toward Interpretable Deep Reinforcement Learning With Linear Model U-trees (2018)
  Guiliang Liu, Oliver Schulte, Wang Zhu, et al.
  13.05
• Measurement-based Adaptation Protocol With Quantum Reinforcement Learning (2018)
  F. Albarrán-Arriagada, J. C. Retamal, E. Solano, et al.
  12.93
• Deep Hierarchical Reinforcement Learning Algorithm In Partially Observable Markov Decision Processes (2018)
  Le Pham Tuyen, Ngo Anh Vien, Abu Layek, et al.
  12.87
• Deep Reinforcement Learning For Adaptive Learning Systems (2020)
  Xiao Li, Hanchen Xu, Jinming Zhang, et al.
  12.54
• A Review Of Uncertainty For Deep Reinforcement Learning (2022)
  Owen Lockwood, Mei Si
  12.47
• Human-level Control Through Directly-trained Deep Spiking Q-networks (2021)
  Guisong Liu, Wenjie Deng, Xiurui Xie, et al.
  12.40
• Explainability In Deep Reinforcement Learning, A Review Into Current Methods And Applications (2022)
  Thomas Hickling, Abdelhafid Zenati, Nabil Aouf, et al.
  12.33
• Combining Evolution And Deep Reinforcement Learning For Policy Search: A Survey (2022)
  Olivier Sigaud
  12.25
• Reinforcement Learning And Its Connections With Neuroscience And Psychology (2020)
  Ajay Subramanian, Sharad Chitlangia, Veeky Baths
  12.25
• Return-based Contrastive Representation Learning For Reinforcement Learning (2021)
  Guoqing Liu, Chuheng Zhang, Li Zhao, et al.
  12.17
• Adaptive Trust Region Policy Optimization: Global Convergence And Faster Rates For Regularized Mdps (2019)
  Lior Shani, Yonathan Efroni, Shie Mannor
  12.10
• An Information-theoretic Perspective On Intrinsic Motivation In Reinforcement Learning: A Survey (2022)
  Arthur Aubret, Laetitia Matignon, Salima Hassas
  11.93
• Local And Global Explanations Of Agent Behavior: Integrating Strategy Summaries With Saliency Maps (2020)
  Tobias Huber, Katharina Weitz, Elisabeth André, et al.
  11.85
• Derivative-free Reinforcement Learning: A Review (2021)
  Hong Qian, Yang Yu
  11.85
• Cell Selection With Deep Reinforcement Learning In Sparse Mobile Crowdsensing (2018)
  Leye Wang, Wenbin Liu, Daqing Zhang, et al.
  11.85
• Actor-critic Network For O-RAN Resource Allocation: Xapp Design, Deployment, And Analysis (2022)
  Mohammadreza Kouchaki, Vuk Marojevic
  11.76
• Convergence Proof For Actor-critic Methods Applied To PPO And RUDDER (2020)
  Markus Holzleitner, Lukas Gruber, José Arjona-Medina, et al.
  11.67