Learning Improved Entertainment Trading Strategies for the TAC Travel Game

For almost five years we have continually operated a simulation testbed exploring a variety of 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