Efficient Strategy Synthesis for MDPs with Resource Constraints

05/05/2021
by   František Blahoudek, et al.
0

We consider qualitative strategy synthesis for the formalism called consumption Markov decision processes. This formalism can model dynamics of an agents that operates under resource constraints in a stochastic environment. The presented algorithms work in time polynomial with respect to the representation of the model and they synthesize strategies ensuring that a given set of goal states will be reached (once or infinitely many times) with probability 1 without resource exhaustion. In particular, when the amount of resource becomes too low to safely continue in the mission, the strategy changes course of the agent towards one of a designated set of reload states where the agent replenishes the resource to full capacity; with sufficient amount of resource, the agent attempts to fulfill the mission again. We also present two heuristics that attempt to reduce expected time that the agent needs to fulfill the given mission, a parameter important in practical planning. The presented algorithms were implemented and numerical examples demonstrate (i) the effectiveness (in terms of computation time) of the planning approach based on consumption Markov decision processes and (ii) the positive impact of the two heuristics on planning in a realistic example.

READ FULL TEXT
05/14/2020

Qualitative Controller Synthesis for Consumption Markov Decision Processes

Consumption Markov Decision Processes (CMDPs) are probabilistic decision...
05/04/2021

Polynomial-Time Algorithms for Multi-Agent Minimal-Capacity Planning

We study the problem of minimizing the resource capacity of autonomous a...
11/29/2019

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation

This paper proposes a formal approach to learning and planning for agent...
03/23/2018

Counterexamples for Robotic Planning Explained in Structured Language

Automated techniques such as model checking have been used to verify mod...
12/03/2020

Verifiable Planning in Expected Reward Multichain MDPs

The planning domain has experienced increased interest in the formal syn...
02/27/2018

Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes

We study planning problems where autonomous agents operate inside enviro...
07/11/2017

Synthesis of Optimal Resilient Control Strategies

Repair mechanisms are important within resilient systems to maintain the...