def policy_iteration(self, v_init=None, max_iter=None):
        if self.beta == 1:
            raise NotImplementedError(self._error_msg_no_discounting)

        if max_iter is None:
            max_iter = self.max_iter

        # What for initial condition?
        if v_init is None:
            v_init = self.s_wise_max(self.R)

        sigma = self.compute_greedy(v_init)
        new_sigma = np.empty(self.num_states, dtype=int)

        for i in range(max_iter):
            # Policy evaluation
            v_sigma = self.evaluate_policy(sigma)
            # Policy improvement
            self.compute_greedy(v_sigma, sigma=new_sigma)
            if np.array_equal(new_sigma, sigma):
                break
            sigma[:] = new_sigma

        num_iter = i + 1

        res = DPSolveResult(v=v_sigma,
                            sigma=sigma,
                            num_iter=num_iter,
                            mc=self.controlled_mc(sigma),
                            method='policy iteration',
                            max_iter=max_iter)

        return res
Solve the optimization problem by policy iteration. See the
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/markov/ddp.py/DiscreteDP.policy_iteration