def METHOD_NAME(self, env, target_step) -> list:
    """
    Exploring the environment for target_step.
    param env: the Environment instance to be explored.
    param target_step: target steps to explore.
    """
    traj_temp = []
    k = 0
    for _ in range(target_step):
        k += 1
        actions = []
        for i in range(self.n_agents):
            action = self.agents[i].select_actions(self.states[i])
            actions.append(action)
        # print(actions)
        next_s, reward, done, _ = env.step(actions)
        traj_temp.append((self.states, reward, done, actions))
        global_done = all(done[i] is True for i in range(self.n_agents))
        if global_done or k > 100:
            state = env.reset()
            k = 0
        else:
            state = next_s
    self.states = state
    return traj_temp