GPU-Accelerated Counterfactual Regret Minimization
Keywords: counterfactual regret minimization, extensive-form games, graphical processing units, imperfect information games, Nash equilibrium
TL;DR: We implemented CFR as dense and sparse matrix and vector operations, achieving orders of magnitude speedups on GPUs compared to OpenSpiel's baselines.
Abstract: Counterfactual regret minimization is a family of algorithms of no-regret learning dynamics capable of solving large-scale imperfect information games. We propose implementing this algorithm as a series of dense and sparse matrix and vector operations, thereby making it highly parallelizable for a graphical processing unit, at a cost of higher memory usage. Our experiments show that our implementation performs up to about 401.2 times faster than OpenSpiel's Python implementation and, on an expanded set of games, up to about 203.6 times faster than OpenSpiel's C++ implementation and the speedup becomes more pronounced as the size of the game being solved grows.
Supplementary Material: zip
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 25
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