• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 112 matches

• 2024
• 
  Alexander Levine, Peter Stone, Amy Zhang:
  Multistep Inverse Is Not All You Need. RLJ 2: 884-925 (2024)
• 
  Rafael Rodríguez-Sánchez, George Konidaris:
  Learning Abstract World Models for Value-preserving Planning with Options. RLJ 4: 1733-1758 (2024)
• 
  Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
  Weight Clipping for Deep Continual and Reinforcement Learning. RLJ 5: 2198-2217 (2024)
• 
  Nan Jiang, Jinzhao Li, Yexiang Xue:
  A Tighter Convergence Proof of Reverse Experience Replay. RLJ 1: 470-480 (2024)
• 
  David Abel, Mark K. Ho, Anna Harutyunyan:
  Three Dogmas of Reinforcement Learning. RLJ 2: 629-644 (2024)
• 
  Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
  Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLJ 5: 2140-2161 (2024)
• 
  Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V. Kulkarni:
  Boosting Soft Q-Learning by Bounding. RLJ 5: 2373-2399 (2024)
• 
  Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron C. Courville:
  Best Response Shaping. RLJ 2: 798-818 (2024)
• 
  Forest Agostinelli, Misagh Soltani:
  Learning Discrete World Models for Heuristic Search. RLJ 4: 1781-1792 (2024)
• 
  Abdulaziz Almuzairee, Nicklas Hansen, Henrik I. Christensen:
  A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning. RLJ 1: 130-157 (2024)
• 
  Nadav Amir, Yael Niv, Angela Langdon:
  States as goal-directed concepts: an epistemic approach to state-representation learning. RLJ 5: 2096-2106 (2024)
• 
  Georgy Antonov, Peter Dayan:
  Exploring Uncertainty in Distributional Reinforcement Learning. RLJ 2: 961-978 (2024)
• 
  Kris De Asis, Richard S. Sutton:
  An Idiosyncrasy of Time-discretization in Reinforcement Learning. RLJ 3: 1306-1316 (2024)
• 
  Zakariae El Asri, Olivier Sigaud, Nicolas Thome:
  Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning. RLJ 2: 693-713 (2024)
• 
  Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
  Online Planning in POMDPs with State-Requests. RLJ 1: 108-129 (2024)
• 
  Alex Ayoub, David Szepesvari, Francesco Zanini, Bryan Chan, Dhawal Gupta, Bruno Castro da Silva, Dale Schuurmans:
  Mitigating the Curse of Horizon in Monte-Carlo Returns. RLJ 2: 563-572 (2024)
• 
  Mohammad Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh:
  Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. RLJ 5: 2461-2491 (2024)
• 
  Jacob Beck, Matthew Thomas Jackson, Risto Vuorio, Zheng Xiong, Shimon Whiteson:
  SplAgger: Split Aggregation for Meta-Reinforcement Learning. RLJ 1: 450-469 (2024)
• 
  Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann:
  Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL. RLJ 4: 1619-1655 (2024)
• 
  Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand:
  PID Accelerated Temporal Difference Algorithms. RLJ 5: 2071-2095 (2024)
• 
  Raphaël Boige, Yannis Flet-Berliac, Lars C. P. M. Quaedvlieg, Arthur Flajolet, Guillaume Richard, Thomas Pierrot:
  PASTA: Pretrained Action-State Transformer Agents. RLJ 3: 1511-1532 (2024)
• 
  Wei-Di Chang, Scott Fujimoto, David Meger, Gregory Dudek:
  Imitation Learning from Observation through Optimal Transport. RLJ 4: 1911-1923 (2024)
• 
  Yuxin Chen, Chen Tang, Thomas Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, Wei Zhan:
  Quantifying Interaction Level Between Agents Helps Cost-efficient Generalization in Multi-agent Reinforcement Learning. RLJ 4: 1950-1964 (2024)
• 
  Rohan Chitnis, Shentao Yang, Alborz Geramifard:
  Sequential Decision-Making for Inline Text Autocomplete. RLJ 2: 946-960 (2024)
• 
  Kartik Choudhary, Dhawal Gupta, Philip S. Thomas:
  ICU-Sepsis: A Benchmark MDP Built from Real Medical Data. RLJ 4: 1546-1566 (2024)
• 
  Daphne Cornelisse, Eugene Vinitsky:
  Human-compatible driving agents through data-regularized self-play reinforcement learning. RLJ 5: 2320-2344 (2024)
• 
  Nicholas E. Corrado, Yuxiao Qu, John U. Balis, Adam Labiosa, Josiah P. Hanna:
  Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning. RLJ 1: 198-215 (2024)
• 
  Davide Corsi, Guy Amir, Andoni Rodríguez, Guy Katz, César Sánchez, Roy Fox:
  Verification-Guided Shielding for Deep Reinforcement Learning. RLJ 4: 1759-1780 (2024)
• 
  Davide Corsi, Davide Camponogara, Alessandro Farinelli:
  Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning. RLJ 3: 1106-1123 (2024)
• 
  Cyrus Cousins, Kavosh Asadi, Elita A. Lobo, Michael Littman:
  On Welfare-Centric Fair Reinforcement Learning. RLJ 3: 1124-1137 (2024)

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