Abstract
This article surveys a stream of work in reinforcement learning (RL) focussed on the online objective. The traditional objective in RL has been to identify a good behavioural policy with as few interactions with an unknown environment as possible, but without explicitly accounting for reward earned along the way. Many sequential decision-making settings, however, require algorithms that can ensure high cumulative reward or low regret during the learning horizon. We discuss regret minimizing learning algorithms in a variety of RL settings, including the basic multi-armed bandit or stateless Markov Decision Process (MDP), structured (parametric and nonparametric) bandits, online learning in the tabula rasa MDP setting, and in structured (parametric and nonparametric) MDPs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
