def evaluate_F(self, F):
        """
        Given a fixed policy F, with the interpretation :math:`u = -F x`, this
        function computes the matrix :math:`P_F` and constant :math:`d_F`
        associated with discounted cost :math:`J_F(x) = x' P_F x + d_F`

        Parameters
        ----------
        F : array_like(float, ndim=2)
            The policy function, a k x n array

        Returns
        -------
        P_F : array_like(float, ndim=2)
            Matrix for discounted cost
        d_F : scalar(float)
            Constant for discounted cost
        K_F : array_like(float, ndim=2)
            Worst case policy
        O_F : array_like(float, ndim=2)
            Matrix for discounted entropy
        o_F : scalar(float)
            Constant for discounted entropy

        """
        # == Simplify names == #
        Q, R, A, B, C = self.Q, self.R, self.A, self.B, self.C
        beta, theta = self.beta, self.theta

        # == Solve for policies and costs using agent 2's problem == #
        K_F, P_F = self.F_to_K(F)
        I = np.identity(self.j)
        H = inv(I - C.T.dot(P_F.dot(C)) / theta)
        d_F = log(det(H))

        # == Compute O_F and o_F == #
        sig = -1.0 / theta
        AO = sqrt(beta) * (A - dot(B, F) + dot(C, K_F))
        O_F = solve_discrete_lyapunov(AO.T, beta * dot(K_F.T, K_F))
        ho = (trace(H - 1) - d_F) / 2.0
        tr = trace(dot(O_F, C.dot(H.dot(C.T))))
        o_F = (ho + beta * tr) / (1 - beta)

        return K_F, P_F, d_F, O_F, o_F