How to Explore your Opponent's Strategy (almost) Optimally
نویسندگان
چکیده
This work presents a lookahead-based exploration strategy for a model-based learning agent that enables exploration of the opponent’s behavior during interaction in a multi-agent system. Instead of holding one model, the model-based agent maintains a mixed opponent model, a distribution over a set of models that reflects its uncertainty about the opponent’s strategy. Every action is evaluated according to its long run contribution to the expected utility and to the knowledge regarding the opponent’s strategy. We present an efficient algorithm that returns an almost optimal exploration strategy against a given mixed model, and a learning method for acquiring a mixed model consistent with the opponent’s past behavior. We report experimental results in the Iterated Prisoner’s Dilemma game that demonstrate the superiority of the lookahead-based exploration strategy over other exploration methods.
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Learn Your Opponent's Strategy (in Polynomial Time)! Conference Item Learn Your Opponent's Strategy(in Polynomial Time)!
Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. Abstract Agents that interact in a distributed environment might increase their utility by behaving optimally given the strategies of the other agents. To ...
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