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
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management
Surya Murthy, John-Paul Clarke, Ufuk Topcu, Zhenyu Gao
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning
Xinchen Han, Hossam Afifi, Michel Marot
Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation
Hamza Bodor, Laurent Carlier
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
Kaiqu Liang, Haimin Hu, Ryan Liu, Thomas L. Griffiths, Jaime Fernández Fisac
Neural Risk-sensitive Satisficing in Contextual Bandits
Shogo Ito, Tatsuji Takahashi, Yu Kono
ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins
Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Houbing Herbert Song
Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences
Aniruddha Srinivas Joshi
FDPP: Fine-tune Diffusion Policy with Human Preference
Yuxin Chen, Devesh K. Jha, Masayoshi Tomizuka, Diego Romeres
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning
Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, David Muñoz-Valero, Giovanni Montana, Luis Jimenez-Linares
Optimization of Link Configuration for Satellite Communication Using Reinforcement Learning
Tobias Rohe, Michael Kölle, Jan Matheis, Rüdiger Höpfl, Leo Sünkel, Claudia Linnhoff-Popien
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving
Guizhe Jin, Zhuoren Li, Bo Leng, Wei Han, Lu Xiong, Chen Sun
READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data
Rohit Sharma, Shanu Kumar, Avinash Kumar
CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing
Emma Cramer, Lukas Jäschke, Sebastian Trimpe
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning
Mingkang Wu, Devin White, Vernon Lawhern, Nicholas R. Waytowich, Yongcan Cao
Online inductive learning from answer sets for efficient reinforcement learning exploration
Celeste Veronese, Daniele Meli, Alessandro Farinelli
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data
Shilong Deng, Zetao Zheng, Hongcai He, Paul Weng, Jie Shao
Efficient Event-based Delay Learning in Spiking Neural Networks
Balázs Mészáros, James C. Knight, Thomas Nowotny
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions
Karine Karine, Benjamin M. Marlin