Learning To Fight

In this project, 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.

References

• Thore Graepel, Ralf Herbrich and Julian Gold. Learning to Fight. Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education. 2004. (Gzipped Postscript) Winner of the Best Paper Prize.