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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
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