np.array([
[[1, 0, 0],
[1, 0, 0],
[0, 0, 1]],
[[1, 0, 0],
[1, 0, 0],
[0, 0, 1]],
[[0, 1, 0],
[0, 1, 0],
[1, 0, 0]]
])
] * 3
equilibrium = dsSolve(payoffMatrices, transitionMatrices,
discountFactors=0.95, plotPath=True)
print(np.round(equilibrium['strategies'],3))
# array([[[0.369 0.298 0.333]
# [0.369 0.298 0.333]]
#
# [[0.257 0.409 0.333]
# [0.480 0.187 0.333]]
#
# [[0.480 0.187 0.333]
# [0.257 0.409 0.333]]])
print(np.round(equilibrium['stateValues'],3))
# array([[ 0. 0. ]
# [ 0.111 -0.111]
# [-0.111 0.111]])
payoffMatrix = np.nan * np.ones((1, nums_a[s]))
for a in range(nums_a[s]):
payoffMatrix[0,a] = u(C[s,a])
payoffMatrices.append( payoffMatrix )
transitionMatrices = []
for s in range(num_k):
transitionMatrix = np.zeros((nums_a[s], num_k))
for a in range(nums_a[s]):
for s_ in range(num_k):
if a == s_:
transitionMatrix[a,s_] = 1
transitionMatrices.append( transitionMatrix )
equilibrium = dsSolve(
payoffMatrices, transitionMatrices, beta,
showProgress=True, plotPath=True)
# Dynamic stochastic game with 13 states, 1 players and 109 actions.
# Initial value for homotopy continuation successfully found.
# ==================================================
# Start homotopy continuation
# Step 7006: t = 29629.89, s = 121765.69, ds = 1000.00
# Final Result: max|y-y_|/ds = 0.0E+00, max|H| = 4.9E-09
# Time elapsed = 0:01:37
# End homotopy continuation
# ==================================================
policies = np.nan * np.ones(num_k)
values = np.nan * np.ones(num_k)
for s in range(num_k):
num_p = 2
(num_a_min, num_a_max) = (2, 2)
(delta_min, delta_max) = (0.9, 0.95)
nums_a = np.random.randint(low=num_a_min, high=num_a_max+1, size=(num_s,num_p))
payoffMatrices = [np.random.random((num_p, *nums_a[s,:])) for s in range(num_s)]
transitionMatrices = [np.random.exponential(scale=1, size=(*nums_a[s,:], num_s)) for s in range(num_s)]
for s in range(num_s):
for index, value in np.ndenumerate(np.sum(transitionMatrices[s], axis=-1)):
transitionMatrices[s][index] *= 1/value
discountFactors = np.random.uniform(low=delta_min, high=delta_max, size=num_p)
equilibrium = dsSolve(payoffMatrices, transitionMatrices, discountFactors,
showProgress=True, plotPath=True)
print(np.round(equilibrium['strategies'], 3))
print(np.round(equilibrium['stateValues'], 3))
## ============================================================================
## end of file
## ============================================================================
# -*- coding: utf-8 -*- import numpy as np from dsGameSolver.gameSolver import dsSolve payoffMatrices = [np.array([[[5, 0], [4, 2]], [[5, 4], [0, 2]]])] equilibrium = dsSolve(payoffMatrices) print(np.round(equilibrium['strategies'], 3)) # array([[[0. 1.] # [0. 1.]]]) print(np.round(equilibrium['stateValues'], 3)) # array([[2. 2.]]) ## ============================================================================ ## end of file ## ============================================================================