GPU-Accelerated Counterfactual Regret Minimization
This accelerates game-solving for AI researchers and practitioners, though it's an incremental engineering improvement rather than a theoretical breakthrough.
The authors tackled the computational bottleneck of counterfactual regret minimization algorithms for imperfect information games by implementing them as parallelizable matrix/vector operations optimized for GPUs, achieving speedups of up to 401.2x over existing Python implementations and 203.6x over C++ implementations.
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.