import numpy as np


def dedicated_1_policy(state, pre_action=1):
    def get_description():
        return "Always select action 1 which does NOOP (no operation)"

    dedicated_1_policy.description = get_description()
    return 1


def dedicated_2_policy(state, pre_action=1):
    def get_description():
        return "Always select action 2 which hits the ball"

    dedicated_1_policy.description = get_description()
    return 2


def dedicated_3_policy(state, pre_action=1):
    def get_description():
        return "Always select action 3 which moves the agent right"

    dedicated_3_policy.description = get_description()
    return 3


def dedicated_4_policy(state, pre_action=1):
    def get_description():
        return "Always select action 4 which moves the agent left"

    dedicated_4_policy.description = get_description()
    return 4


def dedicated_5_policy(state, pre_action=1):
    def get_description():
        return "Always select action 5 which moves the agent right while hiting the ball"

    dedicated_5_policy.description = get_description()
    return 5


def pseudo_random_policy(state, pre_action):
    def get_description():
        return "Select an action among 1 to 6 alternatively"
    pseudo_random_policy.description = get_description()
    return pre_action % 6 + 1


def real_random_policy(state, pre_action=1):
    def get_description():
        return "Select action with a random policy"

    real_random_policy.description = get_description()
    return np.random.choice(range(0, 6)) + 1


# Complete set of dedicated action policies
def dedicated_6_policy(state, pre_action=1):
    def get_description():
        return "Always select action 5 which moves the agent left while hiting the ball"

    dedicated_6_policy.description = get_description()
    return 6