  In agent control issues, the idea of combining reinforcement learning and
planning has attracted much attention. Two methods focus on micro and macro
action respectively. Their advantages would show together if there is a good
cooperation between them. An essential for the cooperation is to find an
appropriate boundary, assigning different functions to each method. Such
boundary could be represented by parameters in a planning algorithm. In this
paper, we create an optimization strategy for planning parameters, through
analysis to the connection of reaction and planning; we also create a
non-gradient method for accelerating the optimization. The whole algorithm can
find a satisfactory setting of planning parameters, making full use of reaction
capability of specific agents.
