Reinforcement Learning Algorithm
Reinforcement learning (RL) algorithms train agents to make optimal decisions by interacting with an environment and maximizing cumulative rewards. Current research emphasizes improving RL's efficiency and stability, focusing on areas like model-based methods incorporating techniques such as Monte Carlo Tree Search, the development of novel algorithms for specific applications (e.g., traffic control, robotics), and addressing challenges in high-dimensional or partially observable environments. The impact of RL spans diverse fields, from optimizing resource allocation in complex systems to developing more effective personalized interventions in healthcare and improving the efficiency of robotic control systems.
181papers
Papers - Page 4
March 25, 2024
March 20, 2024
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
Luca Giamattei, Matteo Biagiola, Roberto Pietrantuono, Stefano Russo, Paolo TonellaPOLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints
Jean-Baptiste Bouvier, Kartik Nagpal, Negar MehrSafety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network
Jiarong Fan, Ariel Liebman, Hao Wang
March 13, 2024
February 26, 2024
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kathy Jang, Nathan Lichtlé, Eugene Vinitsky, Adit Shah, Matthew Bunting, Matthew Nice, Benedetto Piccoli, Benjamin Seibold, Daniel B. Work+4QF-tuner: Breaking Tradition in Reinforcement Learning
Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid
February 22, 2024