# analysis.py
# -----------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


######################
# ANALYSIS QUESTIONS #
######################

# Set the given parameters to obtain the specified policies through
# value iteration.

def question2():
    answerDiscount = 0.9
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    answerNoise = 0.0
=======
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    answerNoise = 0.0
=======
    answerNoise = 0.01
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    return answerDiscount, answerNoise

def question3a():
    answerDiscount = 0.1
    answerNoise = 0
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    answerLivingReward = -1
=======
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    answerLivingReward = -1
=======
    answerLivingReward = 0
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    return answerDiscount, answerNoise, answerLivingReward
    # If not possible, return 'NOT POSSIBLE'

def question3b():
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    answerDiscount = 0.7
    answerNoise = 0.5
    answerLivingReward = -1.2
=======
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    answerDiscount = 0.7
    answerNoise = 0.5
    answerLivingReward = -1.2
=======
    answerDiscount = 0.6
    answerNoise = 0.3
    answerLivingReward = -1
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    return answerDiscount, answerNoise, answerLivingReward
    # If not possible, return 'NOT POSSIBLE'

def question3c():
    answerDiscount = 1
    answerNoise = 0
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    answerLivingReward = -0.5
=======
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    answerLivingReward = -0.5
=======
    answerLivingReward = -0.1
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    return answerDiscount, answerNoise, answerLivingReward
    # If not possible, return 'NOT POSSIBLE'

def question3d():
    answerDiscount = 1
    answerNoise = 0.5
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    answerLivingReward = -0.5
=======
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    answerLivingReward = -0.5
=======
    answerLivingReward = -0.1
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    return answerDiscount, answerNoise, answerLivingReward
    # If not possible, return 'NOT POSSIBLE'

def question3e():
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    answerDiscount = 1
=======
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    answerDiscount = 1
=======
    answerDiscount = 0
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    answerNoise = 0
    answerLivingReward = 1
    return answerDiscount, answerNoise, answerLivingReward
    # If not possible, return 'NOT POSSIBLE'

def question6():
    answerEpsilon = None
    answerLearningRate = None
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    return 'NOT POSSIBLE'
=======
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    return 'NOT POSSIBLE'
=======
    return answerEpsilon, answerLearningRate
>>>>>>> 78cd9fef1a5c95ffb80f2a370ca3b7eff5f83771
>>>>>>> 4f674328a3979632c212fb5330deaf9697529c8d
    # If not possible, return 'NOT POSSIBLE'

if __name__ == '__main__':
    print 'Answers to analysis questions:'
    import analysis
    for q in [q for q in dir(analysis) if q.startswith('question')]:
        response = getattr(analysis, q)()
        print '  Question %s:\t%s' % (q, str(response))
