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
Model-Based Transfer Learning for Contextual Reinforcement Learning
Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu
Non-maximizing policies that fulfill multi-criterion aspirations in expectation
Simon Dima, Simon Fischer, Jobst Heitzig, Joss Oliver
Cooperative Multi-Agent Deep Reinforcement Learning in Content Ranking Optimization
Zhou Qin, Kai Yuan, Pratik Lahiri, Wenyang Liu
Comp-LTL: Temporal Logic Planning via Zero-Shot Policy Composition
Taylor Bergeron, Zachary Serlin, Kevin Leahy
F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
Prajwal Koirala, Cody Fleming
Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning
Amisha Bhaskar, Zahiruddin Mahammad, Sachin R Jadhav, Pratap Tokekar
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
Aida Afshar, Aldo Pacchiano
AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity
Diego Gutierrez-Oribio, Alexandros Stathas, Ioannis Stefanou
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, Peter Stone
Spacecraft inertial parameters estimation using time series clustering and reinforcement learning
Konstantinos Platanitis, Miguel Arana-Catania, Leonardo Capicchiano, Saurabh Upadhyay, Leonard Felicetti
Faster Model Predictive Control via Self-Supervised Initialization Learning
Zhaoxin Li, Letian Chen, Rohan Paleja, Subramanya Nageshrao, Matthew Gombolay
Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization
Xiao Zhou, Chengzhen Meng, Wenru Liu, Zengqi Peng, Ming Liu, Jun Ma
Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
Haozhe Ma, Zhengding Luo, Thanh Vinh Vo, Kuankuan Sima, Tze-Yun Leong
Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
Hui Ma, Bo Zhang, Bo Xu, Jian Wang, Hongfei Lin, Xiao Sun
Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion
Ho Jae Lee, Seungwoo Hong, Sangbae Kim
Backward explanations via redefinition of predicates
Léo Saulières, Martin C. Cooper, Florence Dupin de Saint Cyr
Progressively Label Enhancement for Large Language Model Alignment
Biao Liu, Ning Xu, Xin Geng