diff --git a/quantecon/_lqnash.py b/quantecon/_lqnash.py
index 2b2fbbbe..ed32b2dd 100644
--- a/quantecon/_lqnash.py
+++ b/quantecon/_lqnash.py
@@ -1,10 +1,9 @@
 import numpy as np
 from scipy.linalg import solve
-from .util import check_random_state
 
 
 def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
-          beta=1.0, tol=1e-8, max_iter=1000, random_state=None):
+          beta=1.0, tol=1e-8, max_iter=1000):
     r"""
     Compute the limit of a Nash linear quadratic dynamic game. In this
     problem, player i minimizes
@@ -63,11 +62,6 @@ def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
         This is the tolerance level for convergence
     max_iter : scalar(int), optional(default=1000)
         This is the maximum number of iteratiosn allowed
-    random_state : int or np.random.RandomState/Generator, optional
-        Random seed (integer) or np.random.RandomState or Generator
-        instance to set the initial state of the random number generator
-        for reproducibility. If None, a randomly initialized RandomState
-        is used.
 
     Returns
     -------
@@ -105,13 +99,12 @@ def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
     else:
         k_2 = B2.shape[1]
 
-    random_state = check_random_state(random_state)
     v1 = np.eye(k_1)
     v2 = np.eye(k_2)
     P1 = np.zeros((n, n))
     P2 = np.zeros((n, n))
-    F1 = random_state.standard_normal((k_1, n))
-    F2 = random_state.standard_normal((k_2, n))
+    F1 = np.full((k_1, n), np.inf)
+    F2 = np.full((k_2, n), np.inf)
 
     for it in range(max_iter):
         # update