Title: Problems on distributions generated by Markov Decision processes
Abstract:
Markov decision processes (MDPs) are finite-state probabilistic systems
with both strategic and random choices, hence well-established to model
the interactions between a controller and its randomly responding
environment. An MDP is viewed as a 1/2-player stochastic game played in
rounds when the controller chooses an action, and the environment
chooses a successor according to a fixed probability distribution.
Recently, MDPs have been viewed as generators of sequences of
probability distributions over states, called the distribution-outcome.
In this talk, we briefly explain synchronizing conditions defined on
distribution-outcomes,
which intuitively requires that some (group of) state(s) tend to
accumulate all the probability mass.
We also give hints about distribution-based bisimulation, and a
related problem called
trace-equivalence.