  Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered
learning where Profit Sharing works to plan a prey position in the higher layer
and Q-learning method trains the state-actions to the target in the lower
layer. In this paper, we expanded HMRL to multi-target problem to take the
distance between targets to the consideration. The function, called `AT field',
can estimate the interests for an agent according to the distance between 2
agents and the advantage/disadvantage of the other agent. Moreover, the
knowledge related to state-action rules is extracted by C4.5. The action under
the situation is decided by using the acquired knowledge. To verify the
effectiveness of proposed method, some experimental results are reported.
