def KMR_Markov_matrix_sequential(N, p, epsilon):
    """
    Generate the Markov matrix for the KMR model with *sequential* move

    N: number of players
    p: level of p-dominance for action 1
       = the value of p such that action 1 is the BR for (1-q, q) for any q > p,
         where q (1-q, resp.) is the prob that the opponent plays action 1 (0, resp.)
    epsilon: mutation probability

    References:
        KMRMarkovMatrixSequential is contributed from https://github.com/oyamad
    """
    P = np.zeros((N+1, N+1), dtype=float)
    P[0, 0], P[0, 1] = 1 - epsilon * (1/2), epsilon * (1/2)
    for n in range(1, N):
        P[n, n-1] = \
            (n/N) * (epsilon * (1/2) +
                     (1 - epsilon) * (((n-1)/(N-1) < p) + ((n-1)/(N-1) == p) * (1/2))
                     )
        P[n, n+1] = \
            ((N-n)/N) * (epsilon * (1/2) +
                         (1 - epsilon) * ((n/(N-1) > p) + (n/(N-1) == p) * (1/2))
                         )
        P[n, n] = 1 - P[n, n-1] - P[n, n+1]
    P[N, N-1], P[N, N] = epsilon * (1/2), 1 - epsilon * (1/2)
    return P


# Tests: methods of MarkovChain, mc_compute_stationary #
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/markov/tests/test_core.py/KMR_Markov_matrix_sequential/original