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
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
Xiaochi Qian, Zixuan Xie, Xinyu Liu, Shangtong Zhang
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback
Alireza Rashidi Laleh, Majid Nili Ahmadabadi
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning
Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning
Zhi Luo, Xiyuan Yang, Pan Zhou, Di Wang
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback
Mahsa Sheikholeslami, Navid Mazrouei, Yousof Gheisari, Afshin Fasihi, Matin Irajpour, Ali Motahharynia
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
Yuze Liu, Tingjie Liu, Tiehua Zhang, Youhua Xia, Jinze Wang, Zhishu Shen, Jiong Jin, Fei Richard Yu
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem
David Ge, Hao Ji
Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
Siyu Wang, Shengran Dai, Jianhui Jiang, Shuang Wu, Yufei Peng, Junbin Zhang
Reinforcement Learning with Action Sequence for Data-Efficient Robot Learning
Younggyo Seo, Pieter Abbeel
Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack
Nicole Meng, Caleb Manicke, David Chen, Yingjie Lao, Caiwen Ding, Pengyu Hong, Kaleel Mahmood
Regret-Free Reinforcement Learning for LTL Specifications
Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani
Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
Yannick Metz, David Lindner, Raphaël Baur, Mennatallah El-Assady
Robust Reinforcement Learning under Diffusion Models for Data with Jumps
Chenyang Jiang, Donggyu Kim, Alejandra Quintos, Yazhen Wang
No-regret Exploration in Shuffle Private Reinforcement Learning
Shaojie Bai, Mohammad Sadegh Talebi, Chengcheng Zhao, Peng Cheng, Jiming Chen
Signaling and Social Learning in Swarms of Robots
Leo Cazenille, Maxime Toquebiau, Nicolas Lobato-Dauzier, Alessia Loi, Loona Macabre, Nathanael Aubert-Kato, Anthony Genot, Nicolas Bredeche
Structure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning
Théophile Champion, Marek Grześ, Howard Bowman
Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet
Marnix Suilen, Thom Badings, Eline M. Bovy, David Parker, Nils Jansen
Syllabus: Portable Curricula for Reinforcement Learning Agents
Ryan Sullivan, Ryan Pégoud, Ameen Ur Rahmen, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P. Dickerson