Reinforcement Learning
Reinforcement learning (RL) focuses on training agents to make optimal decisions in an environment by learning through trial and error, aiming to maximize cumulative rewards. Current research emphasizes improving RL's efficiency and robustness, particularly in areas like human-in-the-loop training (e.g., using human feedback to refine models), handling uncertainty and sparse rewards, and scaling to complex tasks (e.g., robotics, autonomous driving). Prominent approaches involve various policy gradient methods, Monte Carlo Tree Search, and the integration of large language models for improved decision-making and task decomposition. These advancements are driving progress in diverse fields, including robotics, game playing, and the development of more human-aligned AI systems.
Papers
Personalization in Human-Robot Interaction through Preference-based Action Representation Learning
Ruiqi Wang, Dezhong Zhao, Dayoon Suh, Ziqin Yuan, Guohua Chen, Byung-Cheol Min
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang
An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
Tizian Jermann, Hendrik Kolvenbach, Fidel Esquivel Estay, Koen Kramer, Marco Hutter
Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments
Julian Gerald Dcruz, Sam Mahoney, Jia Yun Chua, Adoundeth Soukhabandith, John Mugabe, Weisi Guo, Miguel Arana-Catania
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment
Yooseok Lim, Sujee Lee
RLHFuse: Efficient RLHF Training for Large Language Models with Inter- and Intra-Stage Fusion
Yinmin Zhong, Zili Zhang, Bingyang Wu, Shengyu Liu, Yukun Chen, Changyi Wan, Hanpeng Hu, Lei Xia, Ranchen Ming, Yibo Zhu, Xin Jin
RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
Shuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai
Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
Mohammad Al-Sharman, Luc Edes, Bert Sun, Vishal Jayakumar, Mohamed A. Daoud, Derek Rayside, William Melek
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
Shivam Shandilya, Menglin Xia, Supriyo Ghosh, Huiqiang Jiang, Jue Zhang, Qianhui Wu, Victor Rühle
Training Language Models to Self-Correct via Reinforcement Learning
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, Aleksandra Faust
The Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang, Nathan Kallus, Wen Sun
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL
Eduardo Pignatelli, Johan Ferret, Tim Rockäschel, Edward Grefenstette, Davide Paglieri, Samuel Coward, Laura Toni
Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback
Bahador Beigomi, Zheng H. Zhu
Robots that Learn to Safely Influence via Prediction-Informed Reach-Avoid Dynamic Games
Ravi Pandya, Changliu Liu, Andrea Bajcsy
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
Jiuqi Wang, Shangtong Zhang
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
Jonas Günster, Puze Liu, Jan Peters, Davide Tateo