cluster #8

Papers

• State Representation Learning For Control: An Overview (2018)
  Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, et al.
  17.39
• Improving Sample Efficiency In Model-free Reinforcement Learning From Images (2019)
  Denis Yarats, Amy Zhang, Ilya Kostrikov, et al.
  16.99
• Real-time Model Calibration With Deep Reinforcement Learning (2020)
  Yuan Tian, Manuel Arias Chao, Chetan Kulkarni, et al.
  13.74
• Proximal Policy Optimization Via Enhanced Exploration Efficiency (2020)
  Junwei Zhang, Zhenghao Zhang, Shuai Han, et al.
  13.70
• Count-based Exploration With The Successor Representation (2018)
  Marlos C. MacHado, Marc G. Bellemare, Michael Bowling
  13.17
• Return-based Contrastive Representation Learning For Reinforcement Learning (2021)
  Guoqing Liu, Chuheng Zhang, Li Zhao, et al.
  12.17
• Robust Model-free Reinforcement Learning With Multi-objective Bayesian Optimization (2019)
  Matteo Turchetta, Andreas Krause, Sebastian Trimpe
  11.08
• Barc: Backward Reachability Curriculum For Robotic Reinforcement Learning (2018)
  Boris Ivanovic, James Harrison, Apoorva Sharma, et al.
  10.74
• The Utility Of Sparse Representations For Control In Reinforcement Learning (2018)
  Vincent Liu, Raksha Kumaraswamy, Lei Le, et al.
  10.48
• Deep Reinforcement Learning For Stabilization Of Large-scale Probabilistic Boolean Networks (2022)
  Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris, et al.
  10.07
• A Survey On Physics Informed Reinforcement Learning: Review And Open Problems (2023)
  Chayan Banerjee, Kien Nguyen, Clinton Fookes, et al.
  9.76
• Exploration Versus Exploitation In Reinforcement Learning: A Stochastic Control Approach (2018)
  Haoran Wang, Thaleia Zariphopoulou, Xunyu Zhou
  9.76
• Sample-efficient Model-free Reinforcement Learning With Off-policy Critics (2019)
  Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, et al.
  9.60
• Generalized Population-based Training For Hyperparameter Optimization In Reinforcement Learning (2024)
  Hui Bai, Ran Cheng
  9.59
• Unsupervised Representation Learning In Deep Reinforcement Learning: A Review (2022)
  Nicolò Botteghi, Mannes Poel, Christoph Brune
  9.59
• Adaptive Dynamic Programming For Model-free Tracking Of Trajectories With Time-varying Parameters (2019)
  Florian Köpf, Simon Ramsteiner, Michael Flad, et al.
  9.59
• Drl4route: A Deep Reinforcement Learning Framework For Pick-up And Delivery Route Prediction (2023)
  Xiaowei Mao, Haomin Wen, Hengrui Zhang, et al.
  9.41
• Learning Sparse Representations In Reinforcement Learning With Sparse Coding (2017)
  Lei Le, Raksha Kumaraswamy, Martha White
  8.82
• Starformer: Transformer With State-action-reward Representations For Visual Reinforcement Learning (2021)
  Jinghuan Shang, Kumara Kahatapitiya, Xiang Li, et al.
  8.82
• Learning To Identify Critical States For Reinforcement Learning From Videos (2023)
  Haozhe Liu, Mingchen Zhuge, Bing Li, et al.
  8.76
• Self-organization Of Action Hierarchy And Compositionality By Reinforcement Learning With Recurrent Neural Networks (2019)
  Dongqi Han, Kenji Doya, Jun Tani
  8.60
• Inexact Iterative Numerical Linear Algebra For Neural Network-based Spectral Estimation And Rare-event Prediction (2023)
  John Strahan, Spencer C. Guo, Chatipat Lorpaiboon, et al.
  8.35
• Addressing Action Oscillations Through Learning Policy Inertia (2021)
  Chen Chen, Hongyao Tang, Jianye Hao, et al.
  7.81
• Skill-critic: Refining Learned Skills For Hierarchical Reinforcement Learning (2023)
  Ce Hao, Catherine Weaver, Chen Tang, et al.
  7.50
• Alternating Optimisation And Quadrature For Robust Control (2016)
  Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, et al.
  7.16
• Learning Optimal Integration Of Spatial And Temporal Information In Noisy Chemotaxis (2023)
  Albert Alonso, Julius B. Kirkegaard
  6.77
• The Value-improvement Path: Towards Better Representations For Reinforcement Learning (2020)
  Will Dabney, André Barreto, Mark Rowland, et al.
  6.77
• Gradient-aware Model-based Policy Search (2019)
  Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, et al.
  6.77
• Uncertainty Quantification And Exploration For Reinforcement Learning (2019)
  Yi Zhu, Jing Dong, Henry Lam
  6.77
• Containergym: A Real-world Reinforcement Learning Benchmark For Resource Allocation (2023)
  Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers, et al.
  6.34
• Exploiting The Sign Of The Advantage Function To Learn Deterministic Policies In Continuous Domains (2019)
  Matthieu Zimmer, Paul Weng
  6.34
• Towards Model-based Reinforcement Learning For Industry-near Environments (2019)
  Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
  5.84
• Continuous Value Iteration (CVI) Reinforcement Learning And Imaginary Experience Replay (IER) For Learning Multi-goal, Continuous Action And State Space Controllers (2019)
  Andreas Gerken, Michael Spranger
  5.84
• Deterministic Sequencing Of Exploration And Exploitation For Reinforcement Learning (2022)
  Piyush Gupta, Vaibhav Srivastava
  5.84
• Understanding What Affects The Generalization Gap In Visual Reinforcement Learning: Theory And Empirical Evidence (2024)
  Jiafei Lyu, Le Wan, Xiu Li, et al.
  5.84
• Control-oriented Model-based Reinforcement Learning With Implicit Differentiation (2021)
  Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, et al.
  5.84
• Policy Optimization With Model-based Explorations (2018)
  Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, et al.
  5.84
• Deep Reinforcement Learning With Feedback-based Exploration (2019)
  Jan Scholten, Daan Wout, Carlos Celemin, et al.
  5.84
• Gamma-nets: Generalizing Value Estimation Over Timescale (2019)
  Craig Sherstan, Shibhansh Dohare, James MacGlashan, et al.
  5.84
• Clipup: A Simple And Powerful Optimizer For Distribution-based Policy Evolution (2020)
  Nihat Engin Toklu, Paweł Liskowski, Rupesh Kumar Srivastava
  5.84
• Assumed Density Filtering Q-learning (2017)
  Heejin Jeong, Clark Zhang, George J. Pappas, et al.
  5.24
• Zeroth-order Actor-critic: An Evolutionary Framework For Sequential Decision Problems (2022)
  Yuheng Lei, Yao Lyu, Guojian Zhan, et al.
  5.24
• Colored Noise In PPO: Improved Exploration And Performance Through Correlated Action Sampling (2023)
  Jakob Hollenstein, Georg Martius, Justus Piater
  4.52
• Periodic Intra-ensemble Knowledge Distillation For Reinforcement Learning (2020)
  Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
  4.52
• The Impact Of Task Underspecification In Evaluating Deep Reinforcement Learning (2022)
  Vindula Jayawardana, Catherine Tang, Sirui Li, et al.
  4.52
• ACERAC: Efficient Reinforcement Learning In Fine Time Discretization (2021)
  Jakub Łyskawa, Paweł Wawrzyński
  4.52
• STORM: Efficient Stochastic Transformer Based World Models For Reinforcement Learning (2023)
  Weipu Zhang, Gang Wang, Jian Sun, et al.
  4.52
• Measuring The Reliability Of Reinforcement Learning Algorithms (2019)
  Stephanie C. Y. Chan, Samuel Fishman, John Canny, et al.
  4.43
• Distop: Discovering A Topological Representation To Learn Diverse And Rewarding Skills (2021)
  Arthur Aubret, Laetitia Matignon, Salima Hassas
  3.58
• Trade-off On Sim2real Learning: Real-world Learning Faster Than Simulations (2020)
  Jingyi Huang, Yizheng Zhang, Fabio Giardina, et al.
  3.58