Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption

Consumption Markov Decision Processes (CMDPs) are probabilistic decision-making models of resource-constrained systems. We introduce FiMDP, a tool for controller synthesis in CMDPs with LTL objectives expressible by deterministic Büchi automata. The tool implements the recent algorithm for polynomial-time controller synthesis in CMDPs, but extends it with many additional features. On the conceptual level, the tool implements heuristics for improving the expected reachability times of accepting states, and a support for multi-agent task allocation. On the practical level, the tool offers (among other features) a new strategy simulation framework, integration with the Storm model checker, and FiMDPEnv - a new set of CMDPs that model real-world resource-constrained systems. We also present an evaluation of FiMDP on these real-world scenarios.