Exploration

Papers

• Evolutionary Reinforcement Learning: A Survey (2023)
  Hui Bai, Ran Cheng, Yaochu Jin
  13.93
• 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
• Scalable Photonic Reinforcement Learning By Time-division Multiplexing Of Laser Chaos (2018)
  Makoto Naruse, Takatomo Mihana, Hirokazu Hori, et al.
  13.05
• Policy Search In Continuous Action Domains: An Overview (2018)
  Olivier Sigaud, Freek Stulp
  12.74
• Environment Reconstruction With Hidden Confounders For Reinforcement Learning Based Recommendation (2019)
  Wenjie Shang, Yang Yu, Qingyang Li, et al.
  11.93
• Provably Efficient Reinforcement Learning With Linear Function Approximation (2019)
  Chi Jin, Zhuoran Yang, Zhaoran Wang, et al.
  11.76
• A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks (2017)
  Xiao Li, Yao Ma, Calin Belta
  11.58
• Diversity Policy Gradient For Sample Efficient Quality-diversity Optimization (2020)
  Thomas Pierrot, Valentin MacÉ, Félix Chalumeau, et al.
  11.58
• Theoretical Analysis Of Meta Reinforcement Learning: Generalization Bounds And Convergence Guarantees (2024)
  Cangqing Wang, Mingxiu Sui, Dan Sun, et al.
  10.35
• Evolutionary Reinforcement Learning Via Cooperative Coevolutionary Negatively Correlated Search (2020)
  Hu Zhang, Peng Yang, Yanglong Yu, et al.
  9.92
• Exploration Versus Exploitation In Reinforcement Learning: A Stochastic Control Approach (2018)
  Haoran Wang, Thaleia Zariphopoulou, Xunyu Zhou
  9.76
• Offline Reinforcement Learning For Wireless Network Optimization With Mixture Datasets (2023)
  Kun Yang, Cong Shen, Jing Yang, et al.
  9.59
• Intrinsic Fluctuations Of Reinforcement Learning Promote Cooperation (2022)
  Wolfram Barfuss, Janusz Meylahn
  9.23
• Parallel Exploration Via Negatively Correlated Search (2019)
  Peng Yang, Qi Yang, Ke Tang, et al.
  8.60
• Online Meta-learning By Parallel Algorithm Competition (2017)
  Stefan Elfwing, Eiji Uchibe, Kenji Doya
  8.35
• Sampling Efficient Deep Reinforcement Learning Through Preference-guided Stochastic Exploration (2022)
  Wenhui Huang, Cong Zhang, Jingda Wu, et al.
  8.09
• Multi-agent Deep Reinforcement Learning With Human Strategies (2018)
  Thanh Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
  8.09
• Exploration And Incentives In Reinforcement Learning (2021)
  Max Simchowitz, Aleksandrs Slivkins
  8.09
• DSAC: Distributional Soft Actor-critic For Risk-sensitive Reinforcement Learning (2020)
  Xiaoteng Ma, Junyao Chen, Li Xia, et al.
  7.81
• Autoregressive Policies For Continuous Control Deep Reinforcement Learning (2019)
  Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, et al.
  7.50
• Long-term Visitation Value For Deep Exploration In Sparse Reward Reinforcement Learning (2020)
  Simone Parisi, Davide Tateo, Maximilian Hensel, et al.
  7.24
• PNS: Population-guided Novelty Search For Reinforcement Learning In Hard Exploration Environments (2018)
  Qihao Liu, Yujia Wang, Xiaofeng Liu
  7.16
• Fox: Formation-aware Exploration In Multi-agent Reinforcement Learning (2023)
  Yonghyeon Jo, Sunwoo Lee, Junghyuk Yeom, et al.
  6.77
• Uncertainty Quantification And Exploration For Reinforcement Learning (2019)
  Yi Zhu, Jing Dong, Henry Lam
  6.77
• Learning-driven Exploration For Reinforcement Learning (2019)
  Muhammad Usama, Dong Eui Chang
  6.45
• Collaborative Training Of Heterogeneous Reinforcement Learning Agents In Environments With Sparse Rewards: What And When To Share? (2022)
  Alain Andres, Esther Villar-Rodriguez, Javier del Ser
  6.34
• An Efficient Off-policy Reinforcement Learning Algorithm For The Continuous-time LQR Problem (2023)
  Victor G. Lopez, Matthias A. Müller
  6.34
• An Intrinsically-motivated Approach For Learning Highly Exploring And Fast Mixing Policies (2019)
  Mirco Mutti, Marcello Restelli
  6.34
• Exploring The Limits Of Hierarchical World Models In Reinforcement Learning (2024)
  Robin Schiewer, Anand Subramoney, Laurenz Wiskott
  6.34
• MENTOR: Guiding Hierarchical Reinforcement Learning With Human Feedback And Dynamic Distance Constraint (2024)
  Xinglin Zhou, Yifu Yuan, Shaofu Yang, et al.
  6.34
• A Joint Imitation-reinforcement Learning Framework For Reduced Baseline Regret (2022)
  Sheelabhadra Dey, Sumedh Pendurkar, Guni Sharon, et al.
  5.84
• Deterministic Sequencing Of Exploration And Exploitation For Reinforcement Learning (2022)
  Piyush Gupta, Vaibhav Srivastava
  5.84
• A Further Exploration Of Deep Multi-agent Reinforcement Learning With Hybrid Action Space (2022)
  Hongzhi Hua, Guixuan Wen, Kaigui Wu
  5.84
• Policy Optimization With Model-based Explorations (2018)
  Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, et al.
  5.84
• Design Space Exploration Of Approximate Computing Techniques With A Reinforcement Learning Approach (2023)
  Sepide Saeedi, Alessandro Savino, Stefano di Carlo
  5.84
• Deep Reinforcement Learning With Feedback-based Exploration (2019)
  Jan Scholten, Daan Wout, Carlos Celemin, et al.
  5.84
• Overcoming The Sim-to-real Gap: Leveraging Simulation To Learn To Explore For Real-world RL (2024)
  Andrew Wagenmaker, Kevin Huang, Liyiming Ke, et al.
  5.84
• VASE: Variational Assorted Surprise Exploration For Reinforcement Learning (2019)
  Haitao Xu, Brendan McCane, Lech Szymanski
  5.84
• Boosting Exploration In Actor-critic Algorithms By Incentivizing Plausible Novel States (2022)
  Chayan Banerjee, Zhiyong Chen, Nasimul Noman
  5.24
• Situation-dependent Causal Influence-based Cooperative Multi-agent Reinforcement Learning (2023)
  Xiao Du, Yutong Ye, Pengyu Zhang, et al.
  5.24
• On Hard Exploration For Reinforcement Learning: A Case Study In Pommerman (2019)
  Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, et al.
  5.24
• POPO: Pessimistic Offline Policy Optimization (2020)
  Qiang He, Xinwen Hou
  5.24
• Model-based Safe Deep Reinforcement Learning Via A Constrained Proximal Policy Optimization Algorithm (2022)
  Ashish Kumar Jayant, Shalabh Bhatnagar
  5.24
• Robbins-monro Conditions For Persistent Exploration Learning Strategies (2018)
  Dmitry B. Rokhlin
  5.24
• Autotelic Reinforcement Learning: Exploring Intrinsic Motivations For Skill Acquisition In Open-ended Environments (2025)
  Prakhar Srivastava, Jasmeet Singh
  5.24
• Reinforcement Learning By Guided Safe Exploration (2023)
  Qisong Yang, Thiago D. Simão, Nils Jansen, et al.
  5.24
• Graph Exploration For Effective Multi-agent Q-learning (2023)
  Ainur Zhaikhan, Ali H. Sayed
  5.24
• AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback (2026)
  Miaobo Hu et al.
  4.54
• APEX: Autonomous Policy Exploration for Self-Evolving LLM Agents (2026)
  Yibo Li et al.
  4.54