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ATAL
2009
Springer

Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning

11 years 12 months ago
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-theoretic analysis with reinforcement learning. We apply this methodology to the classic Continuous Double Auction game, conducting the most comprehensive CDA strategic study published to date. Empirical game analysis confirms prior findings about the relative performance of known strategies. Reinforcement learning derives new bidding strategies from the empirical equilibrium environment. Iterative application of this approach yields strategies stronger than any other published CDA bidding policy, culminating in a new Nash equilibrium supported exclusively by our learned strategies. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; I.2.11 [Artificial Intelligence]: Multiagent systems; J.4 [Social and Behavioral Sciences]: Economics General Terms Economics, Experimentation Keywords...
L. Julian Schvartzman, Michael P. Wellman
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ATAL
Authors L. Julian Schvartzman, Michael P. Wellman
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