Abstract Number:
3474
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Imon Banerjee (1)
Institutions:
(1) Northwestern University, N/A
First Author:
Presenting Author:
Abstract Text:
A controlled Markov chain (CMC) is a paired process which constitute a Markovian state and a non-Markovian control. The control is a random variable which chooses a transition kernel and the state transitions according to that transition kernel. The recent popularity of model-based offline reinforcement learning has made learning this transition kernel (a.k.a. "model") an important open question. This talk aims to address that through the lenses of an adaptive, non-parametric, estimator. In particular, we will pose the estimator as a solution to a constrained minimax-optimisation problem and explore its finite sample risk bounds. We will also connect it to recent developments in the theory of model selection. Finally we will discuss some examples which illustrate the applicability of our setup on downstream estimation tasks.
Keywords:
Markov chain|Controlled Markov Chain|Non-parametric estimation|Adaptive-estimation|besov-classes|optimisation
Sponsors:
IMS
Tracks:
Statistical Theory
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