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
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control
Devdhar Patel, Hava Siegelmann
Multi-Agent Actor-Critics in Autonomous Cyber Defense
Mingjun Wang, Remington Dechene
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas A Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
Claas A Voelcker, Marcel Hussing, Eric Eaton
Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning
Duo Wang, Maximilien Chau, Andrea Araldo
SOLD: Reinforcement Learning with Slot Object-Centric Latent Dynamics
Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales, Sven Behnke
Words as Beacons: Guiding RL Agents with High-Level Language Prompts
Unai Ruiz-Gonzalez, Alain Andres, Pedro G.Bascoy, Javier Del Ser
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
Xinran Li, Ling Pan, Jun Zhang
Reinforcement Learning for Optimal Control of Adaptive Cell Populations
Josiah C. Kratz, Jacob Adamczyk
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Niclas Boehmer, Yunfan Zhao, Guojun Xiong, Paula Rodriguez-Diaz, Paola Del Cueto Cibrian, Joseph Ngonzi, Adeline Boatin, Milind Tambe
Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning
Tirthankar Mittra
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning
Roberto Barceló, Cristóbal Alcázar, Felipe Tobar
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
William Thorne, Ambrose Robinson, Bohua Peng, Chenghua Lin, Diana Maynard
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers
Jianing Qi, Hao Tang, Zhigang Zhu
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching
Xiaoshan Lin, Sadık Bera Yüksel, Yasin Yazıcıoğlu, Derya Aksaray
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng, Mengyue Yang, Haifeng Zhang, Haitham Bou Ammar, Jun Wang
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Cristian Meo, Mircea Lica, Zarif Ikram, Akihiro Nakano, Vedant Shah, Aniket Rajiv Didolkar, Dianbo Liu, Anirudh Goyal, Justin Dauwels
On the grid-sampling limit SDE
Christian Bender, Nguyen Tran Thuan
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
Minchan Kwon, Gaeun Kim, Jongsuk Kim, Haeil Lee, Junmo Kim
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare
Nan Fang, Guiliang Liu, Wei Gong