cluster #5

Papers

• Sigmoid-weighted Linear Units For Neural Network Function Approximation In Reinforcement Learning (2017)
  Stefan Elfwing, Eiji Uchibe, Kenji Doya
  24.15
• 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
• Distributional Soft Actor-critic: Off-policy Reinforcement Learning For Addressing Value Estimation Errors (2020)
  Jingliang Duan, Yang Guan, Shengbo Eben Li, et al.
  17.77
• T-soft Update Of Target Network For Deep Reinforcement Learning (2020)
  Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo
  13.39
• Toward Interpretable Deep Reinforcement Learning With Linear Model U-trees (2018)
  Guiliang Liu, Oliver Schulte, Wang Zhu, et al.
  13.05
• Scalable Photonic Reinforcement Learning By Time-division Multiplexing Of Laser Chaos (2018)
  Makoto Naruse, Takatomo Mihana, Hirokazu Hori, 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
• Multi-step Reinforcement Learning: A Unifying Algorithm (2017)
  Kristopher de Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, et al.
  12.68
• 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
• Cell Selection With Deep Reinforcement Learning In Sparse Mobile Crowdsensing (2018)
  Leye Wang, Wenbin Liu, Daqing Zhang, et al.
  11.85
• Convergence Proof For Actor-critic Methods Applied To PPO And RUDDER (2020)
  Markus Holzleitner, Lukas Gruber, José Arjona-Medina, et al.
  11.67
• On The Sample Complexity Of Actor-critic Method For Reinforcement Learning With Function Approximation (2019)
  Harshat Kumar, Alec Koppel, Alejandro Ribeiro
  11.49
• Elastic Step DQN: A Novel Multi-step Algorithm To Alleviate Overestimation In Deep Qnetworks (2022)
  Adrian Ly, Richard Dazeley, Peter Vamplew, et al.
  10.85
• Action Candidate Driven Clipped Double Q-learning For Discrete And Continuous Action Tasks (2022)
  Haobo Jiang, Jin Xie, Jian Yang
  10.61
• Finite-sample Analysis Of Nonlinear Stochastic Approximation With Applications In Reinforcement Learning (2019)
  Zaiwei Chen, Sheng Zhang, Thinh T. Doan, et al.
  10.35
• A Quantum System Control Method Based On Enhanced Reinforcement Learning (2023)
  Wenjie Liu, Bosi Wang, Jihao Fan, et al.
  10.35
• Revisiting State Augmentation Methods For Reinforcement Learning With Stochastic Delays (2021)
  Somjit Nath, Mayank Baranwal, Harshad Khadilkar
  10.35
• Deep Q-learning: Theoretical Insights From An Asymptotic Analysis (2020)
  Arunselvan Ramaswamy, Eyke Hüllermeier
  10.35
• WD3: Taming The Estimation Bias In Deep Reinforcement Learning (2020)
  Qiang He, Xinwen Hou
  10.21
• Performing Deep Recurrent Double Q-learning For Atari Games (2019)
  Felipe Moreno-Vera
  10.07
• Deep Reinforcement Learning With Modulated Hebbian Plus Q Network Architecture (2019)
  Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick, et al.
  9.76
• A Comparative Analysis Of Expected And Distributional Reinforcement Learning (2019)
  Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
  9.76
• Measuring And Characterizing Generalization In Deep Reinforcement Learning (2018)
  Sam Witty, Jun Ki Lee, Emma Tosch, et al.
  9.76
• Finite-time Performance Of Distributed Temporal Difference Learning With Linear Function Approximation (2019)
  Thinh T. Doan, Siva Theja Maguluri, Justin Romberg
  9.59
• Qualitative Measurements Of Policy Discrepancy For Return-based Deep Q-network (2018)
  Wenjia Meng, Qian Zheng, Long Yang, et al.
  9.59
• Estimating Scale-invariant Future In Continuous Time (2018)
  Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg, et al.
  9.59
• Deep Q-learning: A Robust Control Approach (2022)
  Balazs Varga, Balazs Kulcsar, Morteza Haghir Chehreghani
  9.23
• Vqc-based Reinforcement Learning With Data Re-uploading: Performance And Trainability (2024)
  Rodrigo Coelho, André Sequeira, Luís Paulo Santos
  8.60
• Simple And Optimal Methods For Stochastic Variational Inequalities, II: Markovian Noise And Policy Evaluation In Reinforcement Learning (2020)
  Georgios Kotsalis, Guanghui Lan, Tianjiao Li
  8.60
• On The Convergence Of Projective-simulation-based Reinforcement Learning In Markov Decision Processes (2019)
  Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder, et al.
  8.35
• A Discrete-time Switching System Analysis Of Q-learning (2021)
  Donghwan Lee, Jianghai Hu, Niao He
  8.35
• Efficient Off-policy Reinforcement Learning Via Brain-inspired Computing (2022)
  Yang Ni, Danny Abraham, Mariam Issa, et al.
  8.35
• Quantum Machine Learning With Glow For Episodic Tasks And Decision Games (2016)
  Jens Clausen, Hans J. Briegel
  8.09
• Lyapunov-based Reinforcement Learning State Estimator (2020)
  Liang Hu, Chengwei Wu, Wei Pan
  8.09
• Sampling Efficient Deep Reinforcement Learning Through Preference-guided Stochastic Exploration (2022)
  Wenhui Huang, Cong Zhang, Jingda Wu, et al.
  8.09
• Neural Temporal-difference And Q-learning Provably Converge To Global Optima (2019)
  Qi Cai, Zhuoran Yang, Jason D. Lee, et al.
  7.81
• Tensor And Matrix Low-rank Value-function Approximation In Reinforcement Learning (2022)
  Sergio Rozada, Santiago Paternain, Antonio G. Marques
  7.81
• Reinforcement Learning In Non-markovian Environments (2022)
  Siddharth Chandak, Pratik Shah, Vivek S Borkar, et al.
  7.50
• Finite Sample Analysis Of Two-time-scale Natural Actor-critic Algorithm (2021)
  Sajad Khodadadian, Thinh T. Doan, Justin Romberg, et al.
  7.50
• Uncertainty-based Out-of-distribution Detection In Deep Reinforcement Learning (2019)
  Andreas Sedlmeier, Thomas Gabor, Thomy Phan, et al.
  7.50
• Policy Optimization With Stochastic Mirror Descent (2019)
  Long Yang, Yu Zhang, Gang Zheng, et al.
  7.50
• Quantum-train-based Distributed Multi-agent Reinforcement Learning (2024)
  Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu, et al.
  7.16
• Quantum Natural Policy Gradients: Towards Sample-efficient Reinforcement Learning (2023)
  Nico Meyer, Daniel D. Scherer, Axel Plinge, et al.
  7.16
• Estimation Error Correction In Deep Reinforcement Learning For Deterministic Actor-critic Methods (2021)
  Baturay Saglam, Enes Duran, Dogan C. Cicek, et al.
  7.16
• A Unified Approach For Multi-step Temporal-difference Learning With Eligibility Traces In Reinforcement Learning (2018)
  Long Yang, Minhao Shi, Qian Zheng, et al.
  6.77
• Nonparametric Bellman Mappings For Reinforcement Learning: Application To Robust Adaptive Filtering (2024)
  Yuki Akiyama, Minh Vu, Konstantinos Slavakis
  6.34
• Deep Ordinal Reinforcement Learning (2019)
  Alexander Zap, Tobias Joppen, Johannes Fürnkranz
  6.34