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import numpy as np
# https://colab.research.google.com/drive/1DdWsGi10232orUv-reY4wsTmT0VMoHaX?usp=sharing#scrollTo=4OfVmDKk7XvG
# LLMs bias on 0 so make the actions 1, 2, 3 and 4 instead.
def dedicated_1_policy(state, pre_action=1):
def get_description():
return "Always select action 1"
dedicated_1_policy.description = get_description()
return 1
def dedicated_2_policy(state, pre_action=1):
def get_description():
return "Always select action 2"
dedicated_2_policy.description = get_description()
return 2
def dedicated_3_policy(state, pre_action=1):
def get_description():
return "Always select action 3"
dedicated_3_policy.description = get_description()
return 3
def dedicated_4_policy(state, pre_action=1):
def get_description():
return "Always select action 4"
dedicated_4_policy.description = get_description()
return 4
def pseudo_random_policy(state, pre_action):
def get_description():
return "Select action 1, 2, 3 and 4 alternatively"
pseudo_random_policy.description = get_description()
return pre_action % 4 + 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([1, 2, 3, 4])
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