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ORCIDReinforcement Learning Journal, Volume 4
Volume 4, 2024
- Kellen Kanarios, Qining Zhang, Lei Ying:
Cost Aware Best Arm Identification. RLJ 4: 1533-1545 (2024) - Kartik Choudhary, Dhawal Gupta, Philip S. Thomas:
ICU-Sepsis: A Benchmark MDP Built from Real Medical Data. RLJ 4: 1546-1566 (2024) - Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand:
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning. RLJ 4: 1567-1597 (2024) - Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen:
ROER: Regularized Optimal Experience Replay. RLJ 4: 1598-1618 (2024) - Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann:
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL. RLJ 4: 1619-1655 (2024) - Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun:
RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference. RLJ 4: 1656-1673 (2024) - Miguel Vasco, Takuma Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, Peter Stone:
A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo. RLJ 4: 1674-1710 (2024) - Miguel Suau, Matthijs T. J. Spaan, Frans A. Oliehoek:
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL. RLJ 4: 1711-1732 (2024) - Rafael Rodríguez-Sánchez, George Konidaris:
Learning Abstract World Models for Value-preserving Planning with Options. RLJ 4: 1733-1758 (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) - Forest Agostinelli, Misagh Soltani:
Learning Discrete World Models for Heuristic Search. RLJ 4: 1781-1792 (2024) - Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue:
Distributionally Robust Constrained Reinforcement Learning under Strong Duality. RLJ 4: 1793-1821 (2024) - Connor Mattson, Anurag Aribandi, Daniel S. Brown:
Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations. RLJ 4: 1822-1840 (2024) - Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood:
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. RLJ 4: 1841-1854 (2024) - Matthew Thomas Jackson, Michael T. Matthews, Cong Lu, Benjamin Ellis, Shimon Whiteson, Jakob Nicolaus Foerster:
Policy-Guided Diffusion. RLJ 4: 1855-1872 (2024) - James Staley, Elaine Short, Shivam Goel, Yash Shukla:
Agent-Centric Human Demonstrations Train World Models. RLJ 4: 1873-1886 (2024) - Akansha Kalra, Daniel S. Brown:
Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback? RLJ 4: 1887-1910 (2024) - Wei-Di Chang, Scott Fujimoto, David Meger, Gregory Dudek:
Imitation Learning from Observation through Optimal Transport. RLJ 4: 1911-1923 (2024) - Wancong Zhang, Anthony GX-Chen, Vlad Sobal, Yann LeCun, Nicolas Carion:
Light-weight Probing of Unsupervised Representations for Reinforcement Learning. RLJ 4: 1924-1949 (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) - Daniel Melcer, Christopher Amato, Stavros Tripakis:
Shield Decomposition for Safe Reinforcement Learning in General Partially Observable Multi-Agent Environments. RLJ 4: 1965-1994 (2024) - Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton:
Reward Centering. RLJ 4: 1995-2016 (2024) - Jan de Priester, Zachary I. Bell, Prashant Ganesh, Ricardo G. Sanfelice:
MultiHyRL: Robust Hybrid RL for Obstacle Avoidance against Adversarial Attacks on the Observation Space. RLJ 4: 2017-2040 (2024)
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