Abstract
For almost five years we continually operated a simulation testbed exploring strategies for the TAC Travel game. Building on techniques developed in our recent study of continuous double auctions, we performed an equilibrium analysis of our testbed data, and employed reinforcement learning in the equilibrium environment to derive a new entertainment strategy for this domain. A second iteration of this process led to further improvements. We thus demonstrate that interleaving empirical game-theoretic analysis with reinforcement learning in an effective method for generating stronger trading strategies in this domain.
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Schvartzman, L.J., Wellman, M.P. (2010). Learning Improved Entertainment Trading Strategies for the TAC Travel Game. In: David, E., Gerding, E., Sarne, D., Shehory, O. (eds) Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. AMEC TADA 2009 2009. Lecture Notes in Business Information Processing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15117-0_14
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DOI: https://doi.org/10.1007/978-3-642-15117-0_14
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