Efficient Strategy Synthesis for MDPs with Resource Constraints
This work addresses resource-constrained planning in stochastic systems, such as robotics or autonomous agents, but is incremental as it builds on existing consumption Markov decision process frameworks.
The authors tackled the problem of synthesizing strategies for agents operating under resource constraints in stochastic environments, presenting algorithms that guarantee goal achievement with probability 1 while avoiding resource exhaustion, and demonstrated effectiveness through reduced computation times and improved planning in realistic examples.
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.