DFA Learning of Opponent Strategies

摘要

This work studies the control of robots in the adversarial world of "Hunt the Wumpus". The hybrid learning algorithm which controls the robots behavior is a combination of a modified RPNI algorithm, and a utility update algorithm. The modified RPNI algorithm is a DFA learning algorithm, to learn opponents' strategies. An utility update algorithm is used to quickly derive a successful conclusion to the mission of the agent using information gleaned from the modified RPNI. 1 Introduction Developing single purpose learning algorithms for manipulating robots in a given domain, has given way to using hybrid learning algorithms that yield a more robust behavior. An approach to combine a planner with a reactive agent to play the game of soccer has been explored in the literature (Sahota 1994). In this paper, we are interested in developing a hybrid learning algorithm to manipulate robots in an adversarial game, often known as "Hunt the Wumpus". In the wumpus world, there are two a...