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
Reinforcement Learning with Lie Group Orientations for Robotics
Martin Schuck, Jan Brüdigam, Sandra Hirche, Angela Schoellig
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling
Arthur Müller, Lukas Vollenkemper
Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera
Hideya Ochiai, Kohki Hashimoto, Takuya Sakamoto, Seiya Watanabe, Ryosuke Hara, Ryo Yagi, Yuji Aizono, Hiroshi Esaki
An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Large-Scale Recommender Systems
Peng Liu, Jiawei Zhu, Cong Xu, Ming Zhao, Bin Wang
UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning
Kathakoli Sengupta, Zhongkai Shagguan, Sandesh Bharadwaj, Sanjay Arora, Eshed Ohn-Bar, Renato Mancuso
Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
Caio Fabio Oliveira da Silva, Azita Dabiri, Bart De Schutter
Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems
Jake Welde, Nishanth Rao, Pratik Kunapuli, Dinesh Jayaraman, Vijay Kumar
DIGIMON: Diagnosis and Mitigation of Sampling Skew for Reinforcement Learning based Meta-Planner in Robot Navigation
Shiwei Feng, Xuan Chen, Zhiyuan Cheng, Zikang Xiong, Yifei Gao, Siyuan Cheng, Sayali Kate, Xiangyu Zhang
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner, Baher Abdulhai
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation
Qiliang Chen, Sepehr Ilami, Nunzio Lore, Babak Heydari
Catch It! Learning to Catch in Flight with Mobile Dexterous Hands
Yuanhang Zhang, Tianhai Liang, Zhenyang Chen, Yanjie Ze, Huazhe Xu
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Enhancing RL Safety with Counterfactual LLM Reasoning
Dennis Gross, Helge Spieker
Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
Sebastian Hirt, Maik Pfefferkorn, Rolf Findeisen
Quantile Regression for Distributional Reward Models in RLHF
Nicolai Dorka
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Anthony Coache, Sebastian Jaimungal
Reinforcement learning-based statistical search strategy for an axion model from flavor
Satsuki Nishimura, Coh Miyao, Hajime Otsuka
Reinforcement Learning with Quasi-Hyperbolic Discounting
S.R. Eshwar, Mayank Motwani, Nibedita Roy, Gugan Thoppe
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning
Amogh Joshi, Adarsh Kumar Kosta, Kaushik Roy