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
Lifelong Reinforcement Learning via Neuromodulation
Sebastian Lee, Samuel Liebana, Claudia Clopath, Will Dabney
Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control
Anh N. Nhu, Ngoc-Anh Le, Shihang Li, Thang D. V. Truong
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
Huajian Xin, Z. Z. Ren, Junxiao Song, Zhihong Shao, Wanjia Zhao, Haocheng Wang, Bo Liu, Liyue Zhang, Xuan Lu, Qiushi Du, Wenjun Gao, Qihao Zhu, Dejian Yang, Zhibin Gou, Z. F. Wu, Fuli Luo, Chong Ruan
Online Behavior Modification for Expressive User Control of RL-Trained Robots
Isaac Sheidlower, Mavis Murdock, Emma Bethel, Reuben M. Aronson, Elaine Schaertl Short
An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation
Jun Wang, Likang Wu, Qi Liu, Yu Yang
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
Ana Fernández del Río, Michael Brennan Leong, Paulo Saraiva, Ivan Nazarov, Aditya Rastogi, Moiz Hassan, Dexian Tang, África Periáñez
Experimental evaluation of offline reinforcement learning for HVAC control in buildings
Jun Wang, Linyan Li, Qi Liu, Yu Yang
Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services
Ana Fernández del Río, Michael Brennan Leong, Paulo Saraiva, Ivan Nazarov, Aditya Rastogi, Moiz Hassan, Dexian Tang, África Periáñez
Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings
África Periáñez, Kathrin Schmitz, Lazola Makhupula, Moiz Hassan, Moeti Moleko, Ana Fernández del Río, Ivan Nazarov, Aditya Rastogi, Dexian Tang
Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems
Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge, Evgenii Genov, Cedric De Cauwer, Thierry Coosemans, Maarten Messagie
Introduction to Reinforcement Learning
Majid Ghasemi, Dariush Ebrahimi
LLMs can Schedule
Henrik Abgaryan, Ararat Harutyunyan, Tristan Cazenave
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object Detection
Matthias Bartolo, Dylan Seychell, Josef Bajada
Value of Information and Reward Specification in Active Inference and POMDPs
Ran Wei
Hierarchical in-Context Reinforcement Learning with Hindsight Modular Reflections for Planning
Chuanneng Sun, Songjun Huang, Dario Pompili
Building Decision Making Models Through Language Model Regime
Yu Zhang, Haoxiang Liu, Feijun Jiang, Weihua Luo, Kaifu Zhang
A Unified Manifold Similarity Measure Enhancing Few-Shot, Transfer, and Reinforcement Learning in Manifold-Distributed Datasets
Sayed W Qayyumi, Laureance F Park, Oliver Obst
Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
Shi Bo, Minheng Xiao
CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning
Victor Augusto Kich, Jair Augusto Bottega, Raul Steinmetz, Ricardo Bedin Grando, Ayano Yorozu, Akihisa Ohya