class ApproximateQLearningAgent:
def __init__(self, eps, alpha, discount, targetPos, actionStrategy="Absolute"):
if actionStrategy == "Absolute":
self.actions = ((-1, -1), (-1, 0), (-1, 1), (1, -1), (1, 0), (1, 1))
elif actionStrategy == "Differential":
self.actions = ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1))
else:
print "Not valid action strategy"
return
self.QValues = LinearApproximator(self.actions, targetPos)
self.eps = eps
self.alpha = alpha
self.discount = discount
def getBestActionMaxQValue(self, state):
bestAction, maxQValue = self.QValues.getBestActionMaxQValue(state)
return (bestAction, maxQValue)
def selectAction(self, state):
r = random.uniform(0, 1)
if r < self.eps:
# print "-- Action selected randomly --"
return self.actions[random.randint(0, len(self.actions) - 1)]
else:
# print "-- Action selected optimally --"
bestAction = self.getBestActionMaxQValue(state)[0]
return bestAction
def update(self, state, action, newState, reward):
self.QValues.updateWeights(state, action, newState, reward, self.discount, self.alpha)
def __init__(self, eps, alpha, discount, targetPos, actionStrategy="Absolute"):
if actionStrategy == "Absolute":
self.actions = ((-1, -1), (-1, 0), (-1, 1), (1, -1), (1, 0), (1, 1))
elif actionStrategy == "Differential":
self.actions = ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1))
else:
print "Not valid action strategy"
return
self.QValues = LinearApproximator(self.actions, targetPos)
self.eps = eps
self.alpha = alpha
self.discount = discount
