  Freeway merging in congested traffic is a significant challenge toward fully
automated driving. Merging vehicles need to decide not only how to merge into a
spot, but also where to merge. We present a method for the freeway merging
based on multi-policy decision making with a reinforcement learning method
called {\em passive actor-critic} (pAC), which learns with less knowledge of
the system and without active exploration. The method selects a merging spot
candidate by using the state value learned with pAC. We evaluate our method
using real traffic data. Our experiments show that pAC achieves 92\% success
rate to merge into a freeway, which is comparable to human decision making.
