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ORCIDReinforcement Learning Journal, Volume 5
Volume 5, 2024
- Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White:
Investigating the Interplay of Prioritized Replay and Generalization. RLJ 5: 2041-2058 (2024) - Bram M. Renting, Thomas M. Moerland, Holger H. Hoos, Catholijn M. Jonker:
Towards General Negotiation Strategies with End-to-End Reinforcement Learning. RLJ 5: 2059-2070 (2024) - Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand:
PID Accelerated Temporal Difference Algorithms. RLJ 5: 2071-2095 (2024) - Nadav Amir, Yael Niv, Angela Langdon:
States as goal-directed concepts: an epistemic approach to state-representation learning. RLJ 5: 2096-2106 (2024) - Wanqiao Xu, Shi Dong, Benjamin Van Roy:
Posterior Sampling for Continuing Environments. RLJ 5: 2107-2122 (2024) - Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy Fox:
Reinforcement Learning from Delayed Observations via World Models. RLJ 5: 2123-2139 (2024) - Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLJ 5: 2140-2161 (2024) - Olivia P. Dizon-Paradis, Stephen E. Wormald, Daniel E. Capecci, Avanti Bhandarkar, Damon L. Woodard:
Resource Usage Evaluation of Discrete Model-Free Deep Reinforcement Learning Algorithms. RLJ 5: 2162-2177 (2024) - Rafael Rafailov, Kyle Beltran Hatch, Anikait Singh, Aviral Kumar, Laura M. Smith, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J. Ball, Jiajun Wu, Sergey Levine, Chelsea Finn:
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. RLJ 5: 2178-2197 (2024) - Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
Weight Clipping for Deep Continual and Reinforcement Learning. RLJ 5: 2198-2217 (2024) - Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii:
A Batch Sequential Halving Algorithm without Performance Degradation. RLJ 5: 2218-2232 (2024) - Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman:
Causal Contextual Bandits with Adaptive Context. RLJ 5: 2233-2263 (2024) - Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran:
Policy Architectures for Compositional Generalization in Control. RLJ 5: 2264-2283 (2024) - Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel:
Semi-Supervised One Shot Imitation Learning. RLJ 5: 2284-2297 (2024) - Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White:
Cross-environment Hyperparameter Tuning for Reinforcement Learning. RLJ 5: 2298-2319 (2024) - Daphne Cornelisse, Eugene Vinitsky:
Human-compatible driving agents through data-regularized self-play reinforcement learning. RLJ 5: 2320-2344 (2024) - Jeremy McMahan, Young Wu, Yudong Chen, Jerry Zhu, Qiaomin Xie:
Inception: Efficiently Computable Misinformation Attacks on Markov Games. RLJ 5: 2345-2358 (2024) - Linfeng Zhao, Lawson L. S. Wong:
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps. RLJ 5: 2359-2372 (2024) - Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin, Rahul V. Kulkarni:
Boosting Soft Q-Learning by Bounding. RLJ 5: 2373-2399 (2024) - Hassan Saber, Odalric-Ambrym Maillard:
Bandits with Multimodal Structure. RLJ 5: 2400-2439 (2024) - Erin J. Talvitie, Zilei Shao, Huiying Li, Jinghan Hu, Jacob Boerma, Rory Zhao, Xintong Wang:
Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning. RLJ 5: 2440-2460 (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) - Christopher K. Zeitler, Kristina Miller, Sayan Mitra, John Schierman, Mahesh Viswanathan:
Optimizing Rewards while meeting $\omega$-regular Constraints. RLJ 5: 2492-2514 (2024)
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