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Online auction scenarios, such as bidding searches on advertising platforms,often require bidders to participate repeatedly in auctions for the same orsimilar items. We design an algorithm for adaptive automatic bidding inrepeated auctions in which the seller and other bidders also update theirstrategies. We apply and improve the opponent modeling algorithm to allowbidders to learn optimal bidding strategies in this multiagent reinforcementlearning environment. The algorithm uses almost no private information aboutthe opponent or restrictions on the strategy space, so it can be extended tomultiple scenarios. Our algorithm improves the utility compared to both staticbidding strategies and dynamic learning strategies. We hope the application ofopponent modeling in auctions will promote the research of automatic biddingstrategies in online auctions and the design of non-incentive compatibleauction mechanisms.

Applying Opponent Modeling for Automatic bidding in Online Repeated Auctions