def learn(self):
"""
Performs Q learning based on observations but also
performs learning on states that are adjacent time periods,
albeit with a slower learning rate.
For example, traffic at 4:30PM on an edge will be somewhat similar to
traffic at 5:00. Hence, we can use an observation at 4:30 to update 5:00.
"""
self.alpha = const.ALPHA
Q.learn(self) #do normal learning
for seq in self.dataset:
self.dataset2.newSequence()
for state, action, reward in seq: #add states of adjacent time periods
#print(state, action, reward)
period = state % const.PERIODS
node = int(state / const.PERIODS)
self.dataset2.addSample(node * const.PERIODS + (period + 1) % const.PERIODS, action, reward)
self.dataset2.addSample(node * const.PERIODS + (period - 1) % const.PERIODS, action, reward)
temp = self.dataset
self.dataset = self.dataset2
self.alpha = const.ALPHA_ADJ_PERIOD
Q.learn(self)
self.dataset = temp
self.dataset2.clear()
#GPSLearner().learn()
def __init__(self, ):
Q.__init__(self, const.ALPHA, const.GAMMA)
self.explorer = FeasibleEpsilonGreedyExplorer(const.EPSILON, const.DECAY)
self.dataset2 = ReinforcementDataSet(1, 1)
def __init__(self, ):
Q.__init__(self, const.ALPHA, const.GAMMA)
self.explorer = FeasibleEpsilonGreedyExplorer(const.EPSILON, const.DECAY)
