Markov Rewards Processes with Impulse Rewards and Absorbing States

05/01/2021
by   Louis Tan, et al.
0

We study the expected accumulated reward for a discrete-time Markov reward model with absorbing states. The rewards are impulse rewards, where a reward ρ_ij is accumulated when transitioning from state i to state j. We derive an explicit, single-letter expression for the expected accumulated reward as a function of the number of time steps n and include in our analysis the limit in which n →∞.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

01/29/2018

Learning the Reward Function for a Misspecified Model

In model-based reinforcement learning it is typical to treat the problem...
03/23/2021

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

In the standard Markov decision process formalism, users specify tasks b...
02/21/2021

Delayed Rewards Calibration via Reward Empirical Sufficiency

Appropriate credit assignment for delay rewards is a fundamental challen...
09/20/2018

Compounding of Wealth in Proof-of-Stake Cryptocurrencies

Proof-of-stake (PoS) is a promising approach for designing efficient blo...
10/12/2017

Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter

In Crowdfunding platforms, people turn their prototype ideas into real p...
05/29/2018

Maximizing Service Reward for Queues with Deadlines

In this paper we consider a real time queuing system with rewards and de...
04/15/2021

Stochastic Processes with Expected Stopping Time

Markov chains are the de facto finite-state model for stochastic dynamic...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.