Multi-agent Systems have emerged as an active sub field of Artificial Intelligence. Machine learning techniques have played a significant role by handling the inherent complexity of such systems. Robotic Soccer is a typical multi-agent system, wherein the challenge is to develop and hone the skills of the agents that take part in the game. For an indepth and sophisticated understanding of the game, soccerplaying agents must possess the capability to learn and acquire low-level skills. These skills can later be put together and used to emulate the expertise of experienced players. This paper describes the use of reinforcement learning, a machine learning technique, to acquire the base level skills of intercepting a moving ball. Results of simulation runs using the Robocup Soccer server have also been presented.