cluster #3

Papers

• Deep TAMER: Interactive Agent Shaping In High-dimensional State Spaces (2017)
  Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, et al.
  14.73
• Counterfactual State Explanations For Reinforcement Learning Agents Via Generative Deep Learning (2021)
  Matthew L. Olson, Roli Khanna, Lawrence Neal, et al.
  13.23
• Meta-reinforcement Learning For The Tuning Of PI Controllers: An Offline Approach (2022)
  Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom, et al.
  12.02
• Environment Reconstruction With Hidden Confounders For Reinforcement Learning Based Recommendation (2019)
  Wenjie Shang, Yang Yu, Qingyang Li, et al.
  11.93
• Improving Interactive Reinforcement Learning: What Makes A Good Teacher? (2019)
  Francisco Cruz, Sven Magg, Yukie Nagai, et al.
  11.19
• Multi-agent Inverse Reinforcement Learning For Certain General-sum Stochastic Games (2018)
  Xiaomin Lin, Stephen C. Adams, Peter A. Beling
  10.97
• A Tutorial On Meta-reinforcement Learning (2023)
  Jacob Beck, Risto Vuorio, Evan Zheran Liu, et al.
  10.85
• Meta-learning Within Projective Simulation (2016)
  Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, et al.
  10.85
• Theoretical Analysis Of Meta Reinforcement Learning: Generalization Bounds And Convergence Guarantees (2024)
  Cangqing Wang, Mingxiu Sui, Dan Sun, et al.
  10.35
• Meta-learning Via Learned Loss (2019)
  Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, et al.
  10.07
• Explaining Online Reinforcement Learning Decisions Of Self-adaptive Systems (2022)
  Felix Feit, Andreas Metzger, Klaus Pohl
  9.59
• Facial Feedback For Reinforcement Learning: A Case Study And Offline Analysis Using The TAMER Framework (2020)
  Guangliang Li, Hamdi Dibeklioğlu, Shimon Whiteson, et al.
  9.59
• Agent-pro: Learning To Evolve Via Policy-level Reflection And Optimization (2024)
  Wenqi Zhang, Ke Tang, Hai Wu, et al.
  9.59
• Human Engagement Providing Evaluative And Informative Advice For Interactive Reinforcement Learning (2020)
  Adam Bignold, Francisco Cruz, Richard Dazeley, et al.
  9.23
• Diffail: Diffusion Adversarial Imitation Learning (2023)
  Bingzheng Wang, Guoqiang Wu, Teng Pang, et al.
  9.13
• Inverse Reinforcement Learning In Contextual Mdps (2019)
  Stav Belogolovsky, Philip Korsunsky, Shie Mannor, et al.
  8.82
• Online Meta-learning By Parallel Algorithm Competition (2017)
  Stefan Elfwing, Eiji Uchibe, Kenji Doya
  8.35
• Lipschitz Lifelong Reinforcement Learning (2020)
  Erwan Lecarpentier, David Abel, Kavosh Asadi, et al.
  8.35
• Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning (2020)
  Lionel Blondé, Pablo Strasser, Alexandros Kalousis
  7.81
• Meta-reinforcement Learning Based On Self-supervised Task Representation Learning (2023)
  Mingyang Wang, Zhenshan Bing, Xiangtong Yao, et al.
  7.81
• Swiftrl: Towards Efficient Reinforcement Learning On Real Processing-in-memory Systems (2024)
  Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna, et al.
  7.50
• Inverse-inverse Reinforcement Learning. How To Hide Strategy From An Adversarial Inverse Reinforcement Learner (2022)
  Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
  7.50
• Reinforcement Learning And Inverse Reinforcement Learning With System 1 And System 2 (2018)
  Alexander Peysakhovich
  7.16
• Competitive Multi-agent Deep Reinforcement Learning With Counterfactual Thinking (2019)
  Yue Wang, Yao Wan, Chenwei Zhang, et al.
  7.16
• Options Of Interest: Temporal Abstraction With Interest Functions (2020)
  Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, et al.
  6.77
• Higher : Improving Instruction Following With Hindsight Generation For Experience Replay (2019)
  Geoffrey Cideron, Mathieu Seurin, Florian Strub, et al.
  6.34
• Hypernetworks For Zero-shot Transfer In Reinforcement Learning (2022)
  Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan, et al.
  6.34
• On The Role Of Weight Sharing During Deep Option Learning (2019)
  Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, et al.
  6.34
• Exploring The Limits Of Hierarchical World Models In Reinforcement Learning (2024)
  Robin Schiewer, Anand Subramoney, Laurenz Wiskott
  6.34
• Rating-based Reinforcement Learning (2023)
  Devin White, Mingkang Wu, Ellen Novoseller, et al.
  6.34
• Reinforcement Learning In System Identification (2022)
  Jose Antonio Martin H., Oscar Fernandez Vicente, Sergio Perez, et al.
  5.84
• Causal Deep Reinforcement Learning Using Observational Data (2022)
  Wenxuan Zhu, Chao Yu, Qiang Zhang
  5.84
• Task Phasing: Automated Curriculum Learning From Demonstrations (2022)
  Vaibhav Bajaj, Guni Sharon, Peter Stone
  5.24
• Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-task Learning (2021)
  Desmond Cai, Shiau Hong Lim, Laura Wynter
  5.24
• Unified Algorithms For RL With Decision-estimation Coefficients: PAC, Reward-free, Preference-based Learning, And Beyond (2022)
  Fan Chen, Song Mei, Yu Bai
  5.24
• In-trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates (2024)
  Shicheng Liu, Minghui Zhu
  5.24
• Misspecification In Inverse Reinforcement Learning (2022)
  Joar Skalse, Alessandro Abate
  5.24
• Deep Interactive Bayesian Reinforcement Learning Via Meta-learning (2021)
  Luisa Zintgraf, Sam Devlin, Kamil Ciosek, et al.
  5.24
• Rewarding The Scientific Process: Process-level Reward Modeling For Agentic Data Analysis (2026)
  Zhisong Qiu, Shuofei Qiao, Kewei Xu, et al.
  5.07
• Iterative Reward Shaping Using Human Feedback For Correcting Reward Misspecification (2023)
  Jasmina Gajcin, James McCarthy, Rahul Nair, et al.
  4.52
• Learning Action Translator For Meta Reinforcement Learning On Sparse-reward Tasks (2022)
  Yijie Guo, Qiucheng Wu, Honglak Lee
  4.52
• A Model-based Approach For Improving Reinforcement Learning Efficiency Leveraging Expert Observations (2024)
  Erhan Can Ozcan, Vittorio Giammarino, James Queeney, et al.
  4.52
• Excluding The Irrelevant: Focusing Reinforcement Learning Through Continuous Action Masking (2024)
  Roland Stolz, Hanna Krasowski, Jakob Thumm, et al.
  4.52
• Meta-q-learning (2019)
  Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, et al.
  3.58
• Sero: Self-supervised Reinforcement Learning For Recovery From Out-of-distribution Situations (2023)
  Chan Kim, Jaekyung Cho, Christophe Bobda, et al.
  3.58
• Ask-ac: An Initiative Advisor-in-the-loop Actor-critic Framework (2022)
  Shunyu Liu, Kaixuan Chen, Na Yu, et al.
  3.58
• Discriminator Soft Actor Critic Without Extrinsic Rewards (2020)
  Daichi Nishio, Daiki Kuyoshi, Toi Tsuneda, et al.
  3.58
• A Bayesian Approach To Policy Recognition And State Representation Learning (2016)
  Adrian Šošić, Abdelhak M. Zoubir, Heinz Koeppl
  3.58
• Harmonydream: Task Harmonization Inside World Models (2023)
  Haoyu Ma, Jialong Wu, Ningya Feng, et al.
  3.46
• Direct Multi-turn Preference Optimization For Language Agents (2024)
  Wentao Shi, Mengqi Yuan, Junkang Wu, et al.
  3.45