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This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. Reinforcement learning has recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and a higher capability for reactive and adaptive behaviors (Connel and Mahadevan 1993). In the reinforcement learning scheme, a robot and an environment are modeled by two synchronized finite-state automatons interacting in discrete-time cyclical processes. The robot senses the current state of the environment and selects an action.