Many real-world auctions are dynamic processes, in which bidders interact and report information over multiple rounds with the auctioneer. The sequential decision making aspect paired with imperfect information renders analyzing the incentive properties of such auctions much more challenging than in the static case. It is clear that bidders often have incentives for manipulation, but the full scope of such strategies is not well-understood. We aim to develop a tool for better understanding the incentive properties in dynamic auctions by using reinforcement learning to learn the optimal strategic behavior for an auction participant. We frame the decision problem as a Markov Decision Process, show its relation to multi-task reinforcement learning and use a soft actor-critic algorithm with experience relabeling to best-respond against several known analytical equilibria as well as to find profitable deviations against exploitable bidder strategies.
In this study, we apply reinforcement learning techniques and propose what we call reinforcement mechanism design to tackle the dynamic pricing problem in sponsored search auctions. In contrast to previous game-theoretical approaches that heavily rely on rationality and common knowledge among the bidders, we take a data-driven approach, and try to learn, over repeated interactions, the set of optimal reserve prices. We implement our approach within the current sponsored search framework of a major search engine: we first train a buyer behavior model, via a real bidding data set, that accuratel…
Game theory has been developed by scientists as a theory of strategic interaction among players who are supposed to be perfectly rational. These strategic interactions might have been presented in an auction, a business negotiation, a chess game, or even in a political conflict aroused between different agents. In this study, the strategic (rational) agents created by reinforcement learning algorithms are supposed to be bidder agents in various types of auction mechanisms such as British Auction, Sealed Bid Auction, and Vickrey Auction designs. Next, the equilibrium points determined by the ag…
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment. We compare different long-term rewards to incentivize the algorithm to maximize individual payoff and overall social welfare. We test the algorithm in two simulated auction games, and demonstrate that 1) our algorithm outperforms two benchmark algorithms in a direct competition, with cost to social welfare, and 2) our algorithm's aggressive competitive behavior ca…
Empirical game-theoretic analysis (EGTA) has recently been applied successfully to analyze the behavior of large numbers of competing traders in a continuous double auction market. Multiagent simulation methods like EGTA are useful for studying complex strategic environments like a stock market, where it is not feasible to solve analytically for the rational behavior of each agent. A weakness of simulation-based methods in strategic settings, however, is that it is typically impossible to prove that the strategy profile assigned to the simulated agents is stable, as in a Nash equilibrium. I pr…
We consider some classical optimization problems in path planning and network transport, and we introduce new auction-based algorithms for their optimal and suboptimal solution. The algorithms are based on mathematical ideas that are related to competitive bidding by persons for objects and the attendant market equilibrium, which underlie auction processes. However, the starting point of our algorithms is different, namely weighted and unweighted path construction in directed graphs, rather than assignment of persons to objects. The new algorithms have several potential advantages over existin…
Combinatorial auctions where agents can bid on bundles of items are desirable because they allow the agents to express complementarity and substitutability between the items. However, expressing one's preferences can require bidding on all bundles. Selective incremental preference elicitation by the auctioneer was recently proposed to address this problem (Conen & Sandholm 2001), but the idea was not evaluated. In this paper we show, experimentally and theoretically, that automated elicitation provides a drastic benefit. In all of the elicitation schemes under study, as the number of items for…
Diffusion auction is a new model in auction design. It can incentivize the buyers who have already joined in the auction to further diffuse the sale information to others via social relations, whereby both the seller's revenue and the social welfare can be improved. Diffusion auctions are essentially non-typical multidimensional mechanism design problems and agents' social relations are complicatedly involved with their bids. In such auctions, incentive-compatibility (IC) means it is best for every agent to honestly report her valuation and fully diffuse the sale information to all her neighbo…
Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；University of Southern California（南加州大学）；Google Research（谷歌研究院）
Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers' valuations for an item depend on the context that describes the item. However, the seller is not aware of the relationship between the context and buyers' valuations, i.e., buyers' preferences. The seller's goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, where the regret is computed against a clairvoyant policy …
SRI International（SRI国际）；University of British Columbia（不列颠哥伦比亚大学）
Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and mulitagent decision problems. When agents value resources in combination rather than in isolation, they must often deliberate about appropriate bidding strategies for a sequence of auctions offering resources of interest. We briefly describe a discrete dynamic programming model for constructing appropriate bidding policies for resources exhibiting both complementarities and substitutability. We then introduce a continuous approximation of this model, assuming that money…
Cloud computing has been an emerging model which aims at allowing customers to utilize computing resources hosted by Cloud Service Providers (CSPs). More and more consumers rely on CSPs to supply computing and storage service on the one hand, and CSPs try to attract consumers on favorable terms on the other. In such competitive cloud computing markets, pricing policies are critical to market efficiency. While CSPs often publish their prices and charge users according to the amount of resources they consume, auction mechanism is rarely applied. In fact a feasible auction mechanism is the most e…
The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain
Independent Researcher（独立研究者）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；HSE University（俄罗斯伏尔加格勒国立高等经济大学）
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
Institute for Data, Systems, and Society（数据、系统与社会研究所）；Massachusetts Institute of Technology（麻省理工学院）；Booth School of Business（博世商学院）；The University of Chicago（芝加哥大学）；Department of Industrial Engineering and Management Sciences（工业工程与管理科学系）；Northwestern University（西北大学）；Department of Statistics and Data Science（统计与数据科学系）；Yale University（耶鲁大学）；Department of EECS, Department of Statistics（电子工程与计算机科学系、统计学系）；University of California Berkeley（加州大学伯克利分校）
Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
Courant Institute of Mathematical Sciences and the Center for Data Science, New York University（Courant数学科学研究所和数据科学中心，纽约大学）；Stern School of Business, New York University（商学院，纽约大学）；Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Yahoo! Research at Verizon Media（Verizon Media的Yahoo!研究）
Shanghai Jiao Tong University（上海交通大学）；University College London（伦敦大学学院）；MediaGamma Ltd（MediaGamma有限公司）；Vlion Inc（Vlion公司）
The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly h…
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of DQN (Deep Q Network) named DASQN. The inputs of the agent are plain-text descriptions of states of a game of incomplete information, i.e. real-time large scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD's online RTB (real-time bidding) advertisin…
