Recent Developments of Reinforcement Learning: Theory, Methods and Applications
Category: International Association for Statistical Computing (IASC)
Proposal Description
In the rapidly evolving era of artificial intelligence, Reinforcement Learning (RL) emerges as a pivotal methodology, driving significant advancements across various domains. This invited session aims to spotlight the integral role of RL within the AI landscape, elucidating its theoretical foundations, methodological innovations, and diverse applications. We will explore how RL leverages statistical principles to infer optimal decision-making strategies, thus underscoring the synergy between statistical inference and learning algorithms. We will present recent developments in RL that showcase the enhancement of learning efficiency and effectiveness, including e.g. robust RL, inverse RL, Bayesian RL, Distributed RL. Additionally, the session will highlight the application of RL in fields such as healthcare, finance, and beyond, demonstrating its versatility and impact. By bridging the gap between statistics and AI, this session aims to foster a deeper understanding of RL's potential to transform theoretical insights into practical solutions, paving the way for future innovations. Session Organizers: Denis Belomestny and Ying Chen