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Efficient Strategy Iteration for Mean Payoff in Markov Decision Processes
Markov decision processes (MDPs) are standard models for probabilistic
systems with non-deterministic behaviours. Mean payoff (or long-run average
reward) provides a mathematically elegant formalism to express performance
related properties. Strategy iteration is one of the solution techniques
applicable in this context. While in many other contexts it is the technique of
choice due to advantages over e.g. value iteration, such as precision or
possibility of domain-knowledge-aware initialization, it is rarely used for
MDPs, since there it scales worse than value iteration. We provide several
techniques that speed up strategy iteration by orders of magnitude for many
MDPs, eliminating the performance disadvantage while preserving all its
advantages