More specifically, the discounted MDP is one of the standard MDPs in reinforcement learning to describe sequential tasks without interruption or restart.

A long-standing goal in artificial intelligence and algorithmic game theory has been to develop a general algorithm which is capable of finding approximate Nash equilibria in large imperfect-information two-player zero-sum games.

Newcomblike decision problems have been studied extensively in the decision theory literature, but they have so far been largely absent in the reinforcement learning literature.

Molecular geometry prediction of flexible molecules, or conformer search, is a longstanding challenge in computational chemistry.

We are addressing only the major comments in this document. In this document, RXCY refers to Comment Y by Reviewer X. We will ensure to make this crystal clear. In contrast, [4] is an end-to-end reinforcement learning architecture and thus time-consuming. The slowness of CELF in IM is also reported in [2].