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ORCIDReinforcement Learning Journal, Volume 2
Volume 2, 2024
- Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu:
Learning to Optimize for Reinforcement Learning. RLJ 2: 481-497 (2024) - Mhairi Dunion, Stefano V. Albrecht:
Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras. RLJ 2: 498-515 (2024) - Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Amos J. Storkey:
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning. RLJ 2: 516-546 (2024) - Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed, Glen Berseth:
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning. RLJ 2: 547-562 (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) - Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart:
A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization. RLJ 2: 573-592 (2024) - Gersi Doko, Guang Yang, Daniel S. Brown, Marek Petrik:
ROIL: Robust Offline Imitation Learning without Trajectories. RLJ 2: 593-605 (2024) - Edan Meyer, Adam White, Marlos C. Machado:
Harnessing Discrete Representations for Continual Reinforcement Learning. RLJ 2: 606-628 (2024) - David Abel, Mark K. Ho, Anna Harutyunyan:
Three Dogmas of Reinforcement Learning. RLJ 2: 629-644 (2024) - Matteo Papini, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli:
Policy Gradient with Active Importance Sampling. RLJ 2: 645-675 (2024) - Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough. RLJ 2: 676-692 (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) - Ho Long Fung, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi:
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems. RLJ 2: 714-732 (2024) - Yu Luo, Fuchun Sun, Tianying Ji, Xianyuan Zhan:
Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies. RLJ 2: 733-762 (2024) - Gaspard Lambrechts, Adrien Bolland, Damien Ernst:
Informed POMDP: Leveraging Additional Information in Model-Based RL. RLJ 2: 763-784 (2024) - Sam Lobel, Ronald Parr:
An Optimal Tightness Bound for the Simulation Lemma. RLJ 2: 785-797 (2024) - Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron C. Courville:
Best Response Shaping. RLJ 2: 798-818 (2024) - Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli:
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning. RLJ 2: 819-839 (2024) - Khurram Javed, Arsalan Sharifnassab, Richard S. Sutton:
SwiftTD: A Fast and Robust Algorithm for Temporal Difference Learning. RLJ 2: 840-863 (2024) - Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas:
The Cliff of Overcommitment with Policy Gradient Step Sizes. RLJ 2: 864-883 (2024) - Alexander Levine, Peter Stone, Amy Zhang:
Multistep Inverse Is Not All You Need. RLJ 2: 884-925 (2024) - Emma Cramer, Bernd Frauenknecht, Ramil Sabirov, Sebastian Trimpe:
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors. RLJ 2: 926-945 (2024) - Rohan Chitnis, Shentao Yang, Alborz Geramifard:
Sequential Decision-Making for Inline Text Autocomplete. RLJ 2: 946-960 (2024) - Georgy Antonov, Peter Dayan:
Exploring Uncertainty in Distributional Reinforcement Learning. RLJ 2: 961-978 (2024) - Marcel Hussing, Jorge A. Mendez, Anisha Singrodia, Cassandra Kent, Eric Eaton:
Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning. RLJ 2: 979-994 (2024) - Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton:
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence. RLJ 2: 995-1018 (2024)
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