We apply reinforcement learning to the problem of finding good policies for a fighting agent in a commercial computer game. The learning agent is trained using the SARSA algorithm for on-policy learning of an action-value function represented by linear and neural network function approximators. We discuss the selection and construction of features, actions, and rewards as well as other design choices necessary to integrate the learning process into the game. The learning agent is trained against the built-in AI of the game with different rewards encouraging aggressive or defensive behaviour. We show that the learning agent finds interesting (and partly near optimal) policies in accordance with the reward functions provided. We also discuss the particular challenges arising in the application of reinforcement learning to the domain of computer games.