DeepAI AI Chat
Log In Sign Up

Mixing Probabilistic and non-Probabilistic Objectives in Markov Decision Processes

04/28/2020
by   Raphaël Berthon, et al.
0

In this paper, we consider algorithms to decide the existence of strategies in MDPs for Boolean combinations of objectives. These objectives are omega-regular properties that need to be enforced either surely, almost surely, existentially, or with non-zero probability. In this setting, relevant strategies are randomized infinite memory strategies: both infinite memory and randomization may be needed to play optimally. We provide algorithms to solve the general case of Boolean combinations and we also investigate relevant subcases. We further report on complexity bounds for these problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/19/2021

Arena-Independent Finite-Memory Determinacy in Stochastic Games

We study stochastic zero-sum games on graphs, which are prevalent tools ...
10/24/2019

Simple Strategies in Multi-Objective MDPs (Technical Report)

We consider the verification of multiple expected reward objectives at o...
07/07/2020

Strategy Complexity of Parity Objectives in Countable MDPs

We study countably infinite MDPs with parity objectives. Unlike in finit...
07/01/2021

Strategy Complexity of Mean Payoff, Total Payoff and Point Payoff Objectives in Countable MDPs

We study countably infinite Markov decision processes (MDPs) with real-v...
03/10/2022

Strategy Complexity of Point Payoff, Mean Payoff and Total Payoff Objectives in Countable MDPs

We study countably infinite Markov decision processes (MDPs) with real-v...
01/11/2019

Life is Random, Time is Not: Markov Decision Processes with Window Objectives

The window mechanism was introduced by Chatterjee et al. [1] to strength...
10/14/2022

Model-checking lock-sharing systems against regular constraints

We study the verification of distributed systems where processes are fin...