MMM-PHC: A Particle-Based Multi-Agent Learning Algorithm

摘要

Learning is one way to determine how agents should act, but learning in multi-agent systems is more difficult than in single-agent systems because other learning agents modify their behavior. We introduce a particle-based algorithm called MMM-PHC. MMM-PHC promotes convergence to Nash equilibria in matrix games using the ideas of maxim in strategies and partial commitment. Partial commitment is implemented by restricting policies to a simplex. Simulations show that MMM-PHC performs on a larger class of games than WoLFPHC.