Training Pac-Man bots using reinforcement learning and case-based reasoning

Video games are an interesting field of study for many artificial intelligence researchers, since many different AI methods can be studied and tested with them, and later those investigations can be applied to many other situations. In this paper we use case based reasoning and reinforcement learning principles to train bots to play the Ms. PacMan vs. Ghosts game. In particular, we use the well-known Q-learning algorithm but replacing the Q-table with a case base. The use of cases allows us to deal with rich game state representation and inject domain knowledge in both the retrieval and the adaptation stages. Our initial experiments show that we can train bots either to reach high scores or to survive for a long time. However, the combination of both goals seems to be a more challenging problem.