Efficient Strategy Synthesis for MDPs With Resource Constraints

• 成果类型: Article
• 署名作者: Blahoudek, Frantisek; Novotny, Petr; Ornik, Melkior; Thangeda, Pranay; Topcu, Ufuk
• 署名单位: University of Texas System; University of Texas Austin; Masaryk University; University of Illinois System; University of Illinois Urbana-Champaign; University of Texas System; University of Texas Austin
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3209612
• 发表日期: 2023
• 页码: 4586-4601
• 关键词: Consumption Markov decision process (CMDP); PLANNING; resource constraints; strategy synthesis

摘要

We consider qualitative strategy synthesis for the formalism called consumption Markov decision processes. This formalism can model the dynamics of an agent 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 toward one of a designated set of reload states where the agent replenishes the resource to full capacity; with a sufficient amount of resource, the agent attempts to fulfill the mission again. We also present two heuristics that attempt to reduce the expected time that the agent needs to fulfill the given mission, a parameter important in practical planning. The presented algorithms were implemented, and the numerical examples demonstrate the effectiveness (in terms of computation time) of the planning approach based on consumption Markov decision processes and the positive impact of the two heuristics on planning in a realistic example.