cluster #0

Papers

• The Shapley Value In Machine Learning (2022)
  Benedek Rozemberczki, Lauren Watson, Péter Bayer, et al.
  17.35
• Tactics Of Adversarial Attack On Deep Reinforcement Learning Agents (2017)
  Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, et al.
  17.32
• Explainable Reinforcement Learning Through A Causal Lens (2019)
  Prashan Madumal, Tim Miller, Liz Sonenberg, et al.
  16.69
• Vulnerability Of Deep Reinforcement Learning To Policy Induction Attacks (2017)
  Vahid Behzadan, Arslan Munir
  15.98
• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)
  George A. Vouros
  15.80
• Interestingness Elements For Explainable Reinforcement Learning: Understanding Agents' Capabilities And Limitations (2019)
  Pedro Sequeira, Melinda Gervasio
  14.55
• Safe Continual Reinforcement Learning In Non-stationary Environments (2026)
  Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, et al.
  12.89
• Explainability In Deep Reinforcement Learning, A Review Into Current Methods And Applications (2022)
  Thomas Hickling, Abdelhafid Zenati, Nabil Aouf, et al.
  12.33
• Local And Global Explanations Of Agent Behavior: Integrating Strategy Summaries With Saliency Maps (2020)
  Tobias Huber, Katharina Weitz, Elisabeth André, et al.
  11.85
• A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks (2017)
  Xiao Li, Yao Ma, Calin Belta
  11.58
• Generation Of Policy-level Explanations For Reinforcement Learning (2019)
  Nicholay Topin, Manuela Veloso
  11.39
• New Challenges In Reinforcement Learning: A Survey Of Security And Privacy (2022)
  Yunjiao Lei, Dayong Ye, Sheng Shen, et al.
  10.85
• Sok: Adversarial Machine Learning Attacks And Defences In Multi-agent Reinforcement Learning (2023)
  Maxwell Standen, Junae Kim, Claudia Szabo
  10.74
• Dual Policy Distillation (2020)
  Kwei-Herng Lai, Daochen Zha, Yuening Li, et al.
  10.61
• Visual Explanation Using Attention Mechanism In Actor-critic-based Deep Reinforcement Learning (2021)
  Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, et al.
  10.21
• Context-aware Safe Reinforcement Learning For Non-stationary Environments (2021)
  Baiming Chen, Zuxin Liu, Jiacheng Zhu, et al.
  9.76
• Enhancing The Robustness Of QMIX Against State-adversarial Attacks (2023)
  Weiran Guo, Guanjun Liu, Ziyuan Zhou, et al.
  9.76
• Temporal-logic-based Reward Shaping For Continuing Reinforcement Learning Tasks (2020)
  Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu, et al.
  9.76
• Reinforcement Learning Under Threats (2018)
  Victor Gallego, Roi Naveiro, David Rios Insua
  9.59
• Reward Shaping For Happier Autonomous Cyber Security Agents (2023)
  Elizabeth Bates, Vasilios Mavroudis, Chris Hicks
  9.23
• Autonomous Self-explanation Of Behavior For Interactive Reinforcement Learning Agents (2018)
  Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, et al.
  9.23
• Knowru: Knowledge Reusing Via Knowledge Distillation In Multi-agent Reinforcement Learning (2021)
  Zijian Gao, Kele Xu, Bo Ding, et al.
  9.23
• Explainable Reinforcement Learning Via A Causal World Model (2023)
  Zhongwei Yu, Jingqing Ruan, Dengpeng Xing
  9.03
• Safe Reinforcement Learning With Dual Robustness (2023)
  Zeyang Li, Chuxiong Hu, Yunan Wang, et al.
  8.60
• Tripletree: A Versatile Interpretable Representation Of Black Box Agents And Their Environments (2020)
  Tom Bewley, Jonathan Lawry
  8.35
• Smoothing Policies And Safe Policy Gradients (2019)
  Matteo Papini, Matteo Pirotta, Marcello Restelli
  7.50
• Mixrts: Toward Interpretable Multi-agent Reinforcement Learning Via Mixing Recurrent Soft Decision Trees (2022)
  Zichuan Liu, Yuanyang Zhu, Zhi Wang, et al.
  7.16
• Funnel-based Reward Shaping For Signal Temporal Logic Tasks In Reinforcement Learning (2022)
  Naman Saxena, Gorantla Sandeep, Pushpak Jagtap
  7.16
• Integrating Policy Summaries With Reward Decomposition For Explaining Reinforcement Learning Agents (2022)
  Yael Septon, Tobias Huber, Elisabeth André, et al.
  7.16
• Explainable Action Advising For Multi-agent Reinforcement Learning (2022)
  Yue Guo, Joseph Campbell, Simon Stepputtis, et al.
  6.77
• Ablation Study Of How Run Time Assurance Impacts The Training And Performance Of Reinforcement Learning Agents (2022)
  Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson, et al.
  6.77
• Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across Mdps (2021)
  Ezgi Korkmaz
  6.77
• Adversary Agnostic Robust Deep Reinforcement Learning (2020)
  Xinghua Qu, Yew-Soon Ong, Abhishek Gupta, et al.
  6.77
• Lexci: A Framework For Reinforcement Learning With Embedded Systems (2023)
  Kevin Badalian, Lucas Koch, Tobias Brinkmann, et al.
  6.34
• BAFFLE: Hiding Backdoors In Offline Reinforcement Learning Datasets (2022)
  Chen Gong, Zhou Yang, Yunpeng Bai, et al.
  6.34
• Optimal Attack And Defense For Reinforcement Learning (2023)
  Jeremy McMahan, Young Wu, Xiaojin Zhu, et al.
  6.34
• Robust Deep Reinforcement Learning With Adaptive Adversarial Perturbations In Action Space (2024)
  Qianmei Liu, Yufei Kuang, Jie Wang
  6.20
• Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes (2023)
  Yotam Amitai, Yael Septon, Ofra Amir
  5.84
• Cautiously-optimistic Knowledge Sharing For Cooperative Multi-agent Reinforcement Learning (2023)
  Yanwen Ba, Xuan Liu, Xinning Chen, et al.
  5.84
• Superstition In The Network: Deep Reinforcement Learning Plays Deceptive Games (2019)
  Philip Bontrager, Ahmed Khalifa, Damien Anderson, et al.
  5.84
• Rethinking Adversarial Attacks In Reinforcement Learning From Policy Distribution Perspective (2025)
  Tianyang Duan, Zongyuan Zhang, Zheng Lin, et al.
  5.84
• Membership Inference Attacks Against Temporally Correlated Data In Deep Reinforcement Learning (2021)
  Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, et al.
  5.84
• MAVIPER: Learning Decision Tree Policies For Interpretable Multi-agent Reinforcement Learning (2022)
  Stephanie Milani, Zhicheng Zhang, Nicholay Topin, et al.
  5.84
• Reinforcement Learning Beyond Expectation (2021)
  Bhaskar Ramasubramanian, Luyao Niu, Andrew Clark, et al.
  5.84
• Improving Robustness Of Deep Reinforcement Learning Agents: Environment Attack Based On The Critic Network (2021)
  Lucas Schott, Hatem Hajri, Sylvain Lamprier
  5.84
• Benchmarl: Benchmarking Multi-agent Reinforcement Learning (2023)
  Matteo Bettini, Amanda Prorok, Vincent Moens
  5.58
• Situation-dependent Causal Influence-based Cooperative Multi-agent Reinforcement Learning (2023)
  Xiao Du, Yutong Ye, Pengyu Zhang, et al.
  5.24
• Reinforcement Learning Of Causal Variables Using Mediation Analysis (2020)
  Tue Herlau, Rasmus Larsen
  5.24
• Physics-informed RL For Maximal Safety Probability Estimation (2024)
  Hikaru Hoshino, Yorie Nakahira
  5.24
• Model-based Safe Deep Reinforcement Learning Via A Constrained Proximal Policy Optimization Algorithm (2022)
  Ashish Kumar Jayant, Shalabh Bhatnagar
  5.24