  Many reinforcement learning exploration techniques are overly optimistic and
try to explore every state. Such exploration is impossible in environments with
the unlimited number of states. I propose to use simulated exploration with an
optimistic model to discover promising paths for real exploration. This reduces
the needs for the real exploration.
