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q learning pas mal avancé

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This commit is contained in:
François Pelletier 2019-04-13 00:43:54 -04:00
parent aca1c3c599
commit 77caa10f89
3 changed files with 74 additions and 30 deletions
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35
reinforcement/analysis.py
View file

@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
# analysis.py
# -----------
# Licensing Information: You are free to use or extend these projects for
@ -25,45 +26,45 @@ def question2():
return answerDiscount, answerNoise
def question3a():
answerDiscount = None
answerNoise = None
answerLivingReward = None
answerDiscount = 0.9
answerNoise = 0.1
answerLivingReward = -3
return answerDiscount, answerNoise, answerLivingReward
# If not possible, return 'NOT POSSIBLE'
def question3b():
answerDiscount = None
answerNoise = None
answerLivingReward = None
answerDiscount = 0.1
answerNoise = 0.1
answerLivingReward = -2
return answerDiscount, answerNoise, answerLivingReward
# If not possible, return 'NOT POSSIBLE'
def question3c():
answerDiscount = None
answerNoise = None
answerLivingReward = None
answerDiscount = 0.9
answerNoise = 0
answerLivingReward = 0
return answerDiscount, answerNoise, answerLivingReward
# If not possible, return 'NOT POSSIBLE'
def question3d():
answerDiscount = None
answerNoise = None
answerLivingReward = None
answerDiscount = 0.1
answerNoise = 0.1
answerLivingReward = 1
return answerDiscount, answerNoise, answerLivingReward
# If not possible, return 'NOT POSSIBLE'
def question3e():
answerDiscount = None
answerNoise = None
answerLivingReward = None
answerDiscount = 0
answerNoise = 0
answerLivingReward = 1
return answerDiscount, answerNoise, answerLivingReward
# If not possible, return 'NOT POSSIBLE'
def question6():
answerEpsilon = None
answerLearningRate = None
return answerEpsilon, answerLearningRate
# If not possible, return 'NOT POSSIBLE'
# return answerEpsilon, answerLearningRate
return 'NOT POSSIBLE'
if __name__ == '__main__':
print 'Answers to analysis questions:'

61
reinforcement/qlearningAgents.py
View file

@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
# qlearningAgents.py
# ------------------
# Licensing Information: You are free to use or extend these projects for
@ -43,6 +44,7 @@ class QLearningAgent(ReinforcementAgent):
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.q_values = {}
def getQValue(self, state, action):
"""
@ -51,8 +53,11 @@ class QLearningAgent(ReinforcementAgent):
or the Q node value otherwise
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
if (state, action) in self.q_values:
return self.q_values.get((state, action))
else:
return 0
# util.raiseNotDefined()
def computeValueFromQValues(self, state):
"""
@ -62,7 +67,14 @@ class QLearningAgent(ReinforcementAgent):
terminal state, you should return a value of 0.0.
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
legal_actions = self.getLegalActions(state)
if len(legal_actions) == 0:
return 0.0
return max([self.getQValue(state, action) for action in legal_actions])
# util.raiseNotDefined()
def computeActionFromQValues(self, state):
"""
@ -71,7 +83,23 @@ class QLearningAgent(ReinforcementAgent):
you should return None.
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
legalActions = self.getLegalActions(state)
if len(legalActions) == 0:
return None
q_values = [self.getQValue(state, action) for action in legalActions]
q_max = max(q_values)
q_max_indices = []
for index, value in enumerate(q_values):
if value == q_max:
q_max_indices.append(index)
return legalActions[random.choice(q_max_indices)]
# util.raiseNotDefined()
def getAction(self, state):
"""
@ -86,11 +114,21 @@ class QLearningAgent(ReinforcementAgent):
"""
# Pick Action
legalActions = self.getLegalActions(state)
action = None
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
return action
"*** YOUR CODE HERE ***"
if len(legalActions) == 0:
return None
best_action = self.computeActionFromQValues(state)
if util.flipCoin(self.epsilon):
# Action aléatoire
return random.choice(legalActions)
else:
# Meilleure action
return best_action
# util.raiseNotDefined()
def update(self, state, action, nextState, reward):
"""
@ -102,7 +140,12 @@ class QLearningAgent(ReinforcementAgent):
it will be called on your behalf
"""
"*** YOUR CODE HERE ***"
util.raiseNotDefined()
q_value = self.getQValue(state, action)
best_value = self.getValue(nextState)
new_q_value = (1-self.alpha)*q_value+self.alpha*(reward+self.discount*best_value)
self.q_values[(state, action)] = new_q_value
self.q_values.update({(state, action): new_q_value})
# util.raiseNotDefined()
def getPolicy(self, state):
return self.computeActionFromQValues(state)

8
reinforcement/valueIterationAgents.py
View file

@ -50,7 +50,7 @@ class ValueIterationAgent(ValueEstimationAgent):
"*** YOUR CODE HERE ***"
states = self.mdp.getStates()
print "__init__ ... states: " + str(states)
# print "__init__ ... states: " + str(states)
for i in range(iterations):
# On reprend les valeurs de l'itération précédente comme référence
@ -107,9 +107,9 @@ class ValueIterationAgent(ValueEstimationAgent):
return None
q_values = [self.computeQValueFromValues(state, action) for action in possibleActions]
print "computeActionFromValues ... q_values: "+str(q_values)
print "index:"+str(q_values.index(max(q_values)))
print "action:"+str(possibleActions[q_values.index(max(q_values))])
# print "computeActionFromValues ... q_values: "+str(q_values)
# print "index:"+str(q_values.index(max(q_values)))
# print "action:"+str(possibleActions[q_values.index(max(q_values))])
return possibleActions[q_values.index(max(q_values))]
def getPolicy(self, state):