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
AI Planning: A Primer and Survey (Preliminary Report)
Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava, Michael Katz, Sylvie Thiébaux
Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning
Runhao Zeng, Dingjie Zhou, Qiwei Liang, Junlin Liu, Hui Li, Changxin Huang, Jianqiang Li, Xiping Hu, Fuchun Sun
Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning with Human-AI Feedback
Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair, Mohit Gupta, Saloni Garg, Anurag Gautam, Snehal Buldeo, Rajiv Ratn Shah
Reinforcement Learning: An Overview
Kevin Murphy
A Temporally Correlated Latent Exploration for Reinforcement Learning
SuMin Oh, WanSoo Kim, HyunJin Kim
Measuring Goal-Directedness
Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt
Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
Zhejun Zhang, Peter Karkus, Maximilian Igl, Wenhao Ding, Yuxiao Chen, Boris Ivanovic, Marco Pavone
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang, Runyi Hu, Xiaoya Li, Tianwei Zhang, Jiwei Li, Fei Wu, Guoyin Wang, Eduard Hovy
Reinforcement Learning from Wild Animal Videos
Elliot Chane-Sane, Constant Roux, Olivier Stasse, Nicolas Mansard
A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
Murad Dawood, Ahmed Shokry, Maren Bennewitz
Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?
Jingzehua Xu, Guanwen Xie, Ziqi Zhang, Xiangwang Hou, Dongfang Ma, Shuai Zhang, Yong Ren, Dusit Niyato
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning
Talha Azfar, Ruimin Ke
Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
John Subosits, Jenna Lee, Shawn Manuel, Paul Tylkin, Avinash Balachandran
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
Yiran Wang, Chenshu Liu, Yunfan Li, Sanae Amani, Bolei Zhou, Lin F. Yang
PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning
Gian Alix, Arian Haghparast, Manos Papagelis
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator
Kaiwen Jiang, Zhen Fu, Junde Guo, Wei Zhang, Hua Chen
Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning
Weisi Fan, Jesse Lane, Qisai Liu, Soumik Sarkar, Tichakorn Wongpiromsarn
Inverse Delayed Reinforcement Learning
Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan, Frank Yang, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu