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
Multimodal Label Relevance Ranking via Reinforcement Learning
Taian Guo, Taolin Zhang, Haoqian Wu, Hanjun Li, Ruizhi Qiao, Xing Sun
Data-Driven Estimation of Conditional Expectations, Application to Optimal Stopping and Reinforcement Learning
George V. Moustakides
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
Siyu Wang, Xiaocong Chen, Lina Yao
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Yongyuan Liang, Tingqiang Xu, Kaizhe Hu, Guangqi Jiang, Furong Huang, Huazhe Xu
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
Haoyuan Jiang, Xuantang Xiong, Ziyue Li, Hangyu Mao, Guanghu Sui, Jingqing Ruan, Yuheng Cheng, Hua Wei, Wolfgang Ketter, Rui Zhao
Balancing the Scales: Reinforcement Learning for Fair Classification
Leon Eshuijs, Shihan Wang, Antske Fokkens
Three Dogmas of Reinforcement Learning
David Abel, Mark K. Ho, Anna Harutyunyan
G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning
Florian Rupp, Kai Eckert
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng
Reinforcement Learning in High-frequency Market Making
Yuheng Zheng, Zihan Ding
Affordance-Guided Reinforcement Learning via Visual Prompting
Olivia Y. Lee, Annie Xie, Kuan Fang, Karl Pertsch, Chelsea Finn
Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Values
Ashwin Ramaswamy, Ransalu Senanayake
AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Bidding
Chang Lei, Huan Lei
Curriculum Is More Influential Than Haptic Information During Reinforcement Learning of Object Manipulation Against Gravity
Pegah Ojaghi, Romina Mir, Ali Marjaninejad, Andrew Erwin, Michael Wehner, Francisco J Valero-Cueva
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods
Riccardo De Santi, Manish Prajapat, Andreas Krause