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
Explaining Reinforcement Learning: A Counterfactual Shapley Values Approach
Yiwei Shi, Qi Zhang, Kevin McAreavey, Weiru Liu
Terracorder: Sense Long and Prosper
Josh Millar, Sarab Sethi, Hamed Haddadi, Anil Madhavapeddy
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning
Khanh Nguyen, Huy Hoang Nguyen, Egor Panfilov, Aleksei Tiulpin
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
Seyeon Kim, Joonhun Lee, Namhoon Cho, Sungjun Han, Wooseop Hwang
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response
Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev
Visual Grounding for Object-Level Generalization in Reinforcement Learning
Haobin Jiang, Zongqing Lu
Scalable Signal Temporal Logic Guided Reinforcement Learning via Value Function Space Optimization
Yiting He, Peiran Liu, Yiding Ji
Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy
Ravi Shankar, Archana Venkataraman
Adaptive Planning with Generative Models under Uncertainty
Pascal Jutras-Dubé, Ruqi Zhang, Aniket Bera
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
Juan C. Rosero, Ivana Dusparic, Nicolás Cardozo
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Michael Kölle, Daniel Seidl, Maximilian Zorn, Philipp Altmann, Jonas Stein, Thomas Gabor
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation
Yicheng Lin, Dandan Zhang, Yun Liu
A Survey on Self-play Methods in Reinforcement Learning
Ruize Zhang, Zelai Xu, Chengdong Ma, Chao Yu, Wei-Wei Tu, Shiyu Huang, Deheng Ye, Wenbo Ding, Yaodong Yang, Yu Wang
Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
Tian Lan, Huan Wang, Caiming Xiong, Silvio Savarese
Reinforcement Learning applied to Insurance Portfolio Pursuit
Edward James Young, Alistair Rogers, Elliott Tong, James Jordon
A Reinforcement Learning Based Motion Planner for Quadrotor Autonomous Flight in Dense Environment
Zhaohong Liu, Wenxuan Gao, Yinshuai Sun, Peng Dong
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
Dickness Kwesiga, Angshuman Guin, Michael Hunter
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network
Jeffrey Redondo, Nauman Aslam, Juan Zhang, Zhenhui Yuan
ProSpec RL: Plan Ahead, then Execute
Liangliang Liu, Yi Guan, BoRan Wang, Rujia Shen, Yi Lin, Chaoran Kong, Lian Yan, Jingchi Jiang