  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.
