def main():
aiType = 3
worldSize = 6
game = Game(aiType, worldSize)
agent = Agent()
pc = None
policy = None
if aiType == 1:
policy = ValueIteration()
pc = PolicyConfiguration(inpRewards=[1, -1, 0, 10, -1],
inpDiscounts=[1, .1, .1],
inpStochastic=[[100, 0, 0], [0, 100, 0],
[0, 0, 100]])
elif aiType == 2:
policy = PolicyIteration()
pc = PolicyConfiguration(inpRewards=[1, -1, 0, 10, -1],
inpDiscounts=[1, .1, .1],
inpStochastic=[[100, 0, 0], [0, 100, 0],
[0, 0, 100]])
elif aiType == 3:
policy = qLearningAgent()
pc = PolicyConfiguration(inpRewards=[0, -1, 0, 10, -1],
inpDiscounts=[1, .1, .1],
inpStochastic=[[100, 0, 0], [0, 100, 0],
[0, 0, 100]],
inpFile="QLValues.p",
inpTrainingLimit=1000)
elif aiType == 4:
policy = approximateQLearning()
pc = PolicyConfiguration(inpRewards=[2, -1, 0, 0, -1],
inpDiscounts=[0.9, .2, .1],
inpStochastic=[[100, 0, 0], [0, 100, 0],
[0, 0, 100]],
inpFile="AQLWeights.json",
inpTrainingLimit=500)
else:
policy = ValueIteration()
pc = PolicyConfiguration()
policy.config = pc
agent.policy = policy
game.agent = agent
game.mainLoop()
def __init__(self, can, direction, inpAIType):
self.flag = 1
self.can = can
self.direction = direction
self.aiType = inpAIType
self.agent = Agent()
pc = None
policy = None
#inpRewards = [food reward, hazard reward, living reward, good location reward, bad location reward]
#good and bad location is only used for qlearning
#tried to use to cause graph searching
#not really used and can give wonky results
#inpDiscounts = [gamma discount, alpha discount, epsilon explore chance]
#inpStochastic = [forward action[forward chance, left chance, right chance]
#left action[forward chance, left chance, right chance]
#right action[forward chance, left chance, right chance]]
#inpFile file for weight or qvalues
if self.aiType == 1:
policy = ValueIteration()
pc = PolicyConfiguration(inpRewards = [1,-1,0,10,-1], inpDiscounts = [1,.1,.1], inpStochastic = [[100,0,0],[0,100,0],[0,0,100]])
elif self.aiType == 2:
policy = PolicyIteration()
pc = PolicyConfiguration(inpRewards = [1,-1,0,10,-1], inpDiscounts = [1,.1,.1], inpStochastic = [[100,0,0],[0,100,0],[0,0,100]])
elif self.aiType == 3:
policy = qLearningAgent()
#risk aversion aka rarely go off best path seems to work best
#This one seemed to work #pc = PolicyConfiguration(inpRewards = [2,-1,0,0,-1], inpDiscounts = [0.9,.2,.1], inpStochastic = [[100,0,0],[0,100,0],[0,0,100]])
pc = PolicyConfiguration(inpRewards = [2,-1,0,0,0], inpDiscounts = [0.9,.2,.1], inpStochastic = [[100,0,0],[0,100,0],[0,0,100]], inpFile = None, inpTrainingLimit = 20000)
elif self.aiType == 4:
policy = approximateQLearning()
pc = PolicyConfiguration(inpRewards = [2,-1,0,0,-1], inpDiscounts = [0.9,.2,.1], inpStochastic = [[100,0,0],[0,100,0],[0,0,100]], inpFile = None, inpTrainingLimit = 5000)
else:
policy = ValueIteration()
pc = PolicyConfiguration()
policy.config = pc
self.agent.policy = policy