  We propose a new approach to the problem of searching a space of policies for
a Markov decision process (MDP) or a partially observable Markov decision
process (POMDP), given a model. Our approach is based on the following
observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which
all state transitions (given the current state and action) are deterministic.
This reduces the general problem of policy search to one in which we need only
consider POMDPs with deterministic transitions. We give a natural way of
estimating the value of all policies in these transformed POMDPs. Policy search
is then simply performed by searching for a policy with high estimated value.
We also establish conditions under which our value estimates will be good,
recovering theoretical results similar to those of Kearns, Mansour and Ng
(1999), but with "sample complexity" bounds that have only a polynomial rather
than exponential dependence on the horizon time. Our method applies to
arbitrary POMDPs, including ones with infinite state and action spaces. We also
present empirical results for our approach on a small discrete problem, and on
a complex continuous state/continuous action problem involving learning to ride
a bicycle.
