Feb. 13, 2024, 5:45 a.m. | Lei Zhao Mengdi Wang Yu Bai

cs.LG updates on arXiv.org arxiv.org

Inverse Reinforcement Learning (IRL) -- the problem of learning reward functions from demonstrations of an \emph{expert policy} -- plays a critical role in developing intelligent systems. While widely used in applications, theoretical understandings of IRL present unique challenges and remain less developed compared with standard RL. For example, it remains open how to do IRL efficiently in standard \emph{offline} settings with pre-collected data, where states are obtained from a \emph{behavior policy} (which could be the expert policy itself), and actions …

applications challenges cs.ai cs.lg example expert functions intelligent intelligent systems perspective policy reinforcement reinforcement learning role standard stat.ml systems

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Director of Machine Learning

@ Axelera AI | Hybrid/Remote - Europe (incl. UK)

Senior Data Scientist - Trendyol Milla

@ Trendyol | Istanbul (All)

Data Scientist, Mid

@ Booz Allen Hamilton | USA, CA, San Diego (1615 Murray Canyon Rd)

Systems Development Engineer , Amazon Robotics Business Applications and Solutions Engineering

@ Amazon.com | Boston, Massachusetts, USA