def METHOD_NAME(self) -> List[EnvironmentStep]:
    """
    Updates the policies, steps the environments, and returns the step information from the environments.
    Calling code should pass the returned EnvironmentSteps to process_steps() after calling this.
    :return: The list of EnvironmentSteps
    """
    # If we had just reset, process the first EnvironmentSteps.
    # Note that we do it here instead of in reset() so that on the very first reset(),
    # we can create the needed AgentManagers before calling advance() and processing the EnvironmentSteps.
    if self.first_step_infos:
        self._process_step_infos(self.first_step_infos)
        self.first_step_infos = []
    # Get new policies if found. Always get the latest policy.
    for brain_name in self.agent_managers.keys():
        _policy = None
        try:
            # We make sure to empty the policy queue before continuing to produce steps.
            # This halts the trainers until the policy queue is empty.
            while True:
                _policy = self.agent_managers[brain_name].policy_queue.get_nowait()
        except AgentManagerQueue.Empty:
            if _policy is not None:
                self.set_policy(brain_name, _policy)
    # Step the environments
    new_step_infos = self._step()
    return new_step_infos