mlsanityData to 14 Oct 2026

Lecture Notes on Partially Known MDPs

arXiv 2112.02976 ↗First posted 6 Dec 2021cs.AI cs.FLcited by 0

Not on the map. Why?

Abstract (arXiv)

In these notes we will tackle the problem of finding optimal policies for Markov decision processes (MDPs) which are not fully known to us. Our intention is to slowly transition from an offline setting to an online (learning) setting. Namely, we are moving towards reinforcement learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 17 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

3 of 17 references.

References · 3

  1. 2017Parametric Markov Chains: PCTL Complexity and Fraction-free Gaussian Eliminationcited by 11
  2. 2020The Complexity of Reachability in Parametric Markov Decision Processescited by 10
  3. 2018Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraintscited by 8

Built on by

Not cited yet.