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Learning in Games

Fictitious play and regret matching convergence

Run fictitious play and regret matching algorithms. Watch empirical frequencies converge to Nash equilibrium in zero-sum games, and observe the characteristic cycling and spiraling patterns.

HeadsTails
Heads1, -1-1, 1
Tails-1, 11, -1
Final p(Heads): 0.537Final q(Heads): 0.493Nash: p*=0.500, q*=0.500

Strategy Probabilities Over Time

p (Row plays Heads)q (Col plays Heads)
50100150200250300round0.000.250.500.751.00mixing probability

Strategy Space Trajectory

0.000.250.500.751.00q (prob. Column plays Heads)0.000.250.500.751.00p (prob. Row plays Heads)

Fictitious play converges to Nash in zero-sum games. Regret matching converges to the set of correlated equilibria. The hollow circle marks the mixed Nash equilibrium.

Learning in Games — Game Theory Labs · hbar.university