论文标题
在拍卖模拟中使用多代理强化学习
Using Multi-Agent Reinforcement Learning in Auction Simulations
论文作者
论文摘要
科学家已经开发了游戏理论，作为在应该是完全理性的玩家中的战略互动理论。这些战略互动可能是在拍卖，商业谈判，国际象棋游戏甚至在不同代理之间引起的政治冲突中提出的。在这项研究中，通过增强学习算法创建的战略（理性）代理被认为是各种拍卖机制（例如英国拍卖，密封竞标拍卖和Vickrey拍卖设计）中的投标药。接下来，将由代理确定的平衡点与这些环境的NASH平衡点的结果进行比较。根据个人合理性，真实性（防策略）和计算效率，分析了代理商的投标策略。结果表明，使用多机构增强学习策略可改善拍卖模拟的结果。
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 agents are compared with the outcomes of the Nash equilibrium points for these environments. The bidding strategy of the agents is analyzed in terms of individual rationality, truthfulness (strategy-proof), and computational efficiency. The results show that using a multi-agent reinforcement learning strategy improves the outcomes of the auction simulations.
