Improvement in Game Agent Control Using State-Action Value Scaling

The aim of this paper is to enhance the performance of a reinforcement learning game agent controller, within a dynamic game environment, through the retention of learned information over a series of consecutive games. Using a variation of the classic arcade game Pac-Man, the Sarsa algorithm has been utilised for the control of the Pac-Man game agent. The results indicate the use of stateaction value scaling between games played as successful in preserving prior knowledge, therefore improving the performance of the game agent when a series of consecutive games are played.