Learning Character Behaviors using Agent Modeling in Games

Learning Character Behaviors using Agent Modeling in Games

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by Richard Zhao , Duane Szafron

BibTeX

@MISC{Zhao_learningcharacter,
    author = {Richard Zhao and Duane Szafron},
    title = {Learning Character Behaviors using Agent Modeling in Games},
    year = {}
}

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Abstract

Our goal is to provide learning mechanisms to game agents so they are capable of adapting to new behaviors based on the actions of other agents. We introduce a new on-line reinforcement learning (RL) algorithm, ALeRT-AM, that includes an agent-modeling mechanism. We implemented this algorithm in BioWare Corp.’s role-playing game, Neverwinter Nights to evaluate its effectiveness in a real game. Our experiments compare agents who use ALeRT-AM with agents that use the non-agent modeling ALeRT RL algorithm and two other non-RL algorithms. We show that an ALeRT-AM agent is able to rapidly learn a winning strategy against other agents in a combat scenario and to adapt to changes in the environment.

Keyphrases

character behavior agent modeling combat scenario game agent alert-am agent agent-modeling mechanism non-agent modeling alert rl algorithm new on-line reinforcement learning neverwinter night real game bioware corp role-playing game non-rl algorithm new behavior