A Singaporean man has lost a court case to claw back hundreds of thousands of dollars that he had lavished on his ex-girlfriend while they were together. The S$468,000 ($369,200; £273,260) that Chander Agarwal attempted to recover from Felicia Lee was money he had willingly spent on her, the Singapore High Court ruled. Agarwal displayed a consistent pattern of showering [Lee] with lavish gifts even before they got together and would reassure her that he did not expect repayment, Senior Judge Lee Seiu Kin said. Agarwal, the CEO of a logistics company, met Felicia Lee, a former flight attendant, during a flight in 2019. They were in a romantic relationship from September 2022 to December 2023, the court heard.

Frequently,we are also interested in the scores associated with the objects to deduce the intensity of the resulting preference.

We then give a practical approximation using neural networks anddemonstrate itsperformance andsampleefficiencyinpractice.

Insomework,functionapproximation scheme is adopted such that essential quantities for policy improvement, e.g.

Then, we consider sufficient assumptions under which learning good policies requires polynomial number of episodes.

On the technical side, we show that the logarithmic loss and an informationtheoretic quantity called thetriangular discriminationplay a fundamental role in obtaining first-order guarantees, and we combine this observation with new refinements tothe regression oracle reduction framework ofFoster and Rakhlin [29].

This paper focuses on MDPs with linearly realizable optimal Q-functionQ?.

O((|S ||A|+ T) H ( log ( 1/")/")). ItisalsoeasyV ( , )andQ ( , , )obtained algorithm.

There, areward function isdrawn from one of multiple possible reward models atthebeginning ofeveryepisode, buttheidentity ofthechosen rewardmodel is not revealed to the agent. Hence, the latent state space, for which the dynamics are Markovian, is not given to the agent. We study the problem of learning a near optimal policy for two reward-mixing MDPs. Unlike existing approaches that rely on strong assumptions on the dynamics, we make no assumptions and study the problem in full generality.