  Symmetry breaking is a common approach for model reduction of Markov decision
processes (MDPs). This approach only uses directly accessible symmetries such
as geometric symmetries. For some MDPs, it is possible to transform them
equivalently such that symmetries become accessible -- we call this type of
symmetries hidden symmetries. For these MDPs, hidden symmetries allow
substantially better model reduction compared to directly accessible
symmetries. The main idea is to reveal a hidden symmetry by altering the reward
structure and then exploit the revealed symmetry by forming a quotient MDP. The
quotient MDP is the reduced MDP, since it is sufficient to solve the quotient
MDP instead of the original one. In this paper, we introduce hidden symmetries
and the associated concept of model reduction. We demonstrate this concept on
the multi-period newsvendor problem, which is the newsvendor problem considered
over an infinite number of days. In this way, we show that hidden symmetries
can reduce problems that directly accessible symmetries cannot, and present a
basic idea of revealing hidden symmetries in multi-period problems. The
presented approach can be extended to more sophisticated problems.
