def __init__(self, mode):
self.mode = mode
cu.mem('Reinforcement Learning Started')
self.environment = RegionFilteringEnvironment(
config.get(mode + 'Database'), mode)
self.controller = QNetwork()
cu.mem('QNetwork controller created')
self.learner = None
self.agent = RegionFilteringAgent(self.controller, self.learner)
self.task = RegionFilteringTask(self.environment,
config.get(mode + 'GroundTruth'))
self.experiment = Experiment(self.task, self.agent)
def __init__(self, mode):
self.mode = mode
cu.mem('Reinforcement Learning Started')
self.environment = RegionFilteringEnvironment(config.get(mode+'Database'), mode)
self.controller = QNetwork()
cu.mem('QNetwork controller created')
self.learner = None
self.agent = RegionFilteringAgent(self.controller, self.learner)
self.task = RegionFilteringTask(self.environment, config.get(mode+'GroundTruth'))
self.experiment = Experiment(self.task, self.agent)
class ReinforcementLearningRunner():
def __init__(self, mode):
self.mode = mode
cu.mem('Reinforcement Learning Started')
self.environment = RegionFilteringEnvironment(
config.get(mode + 'Database'), mode)
self.controller = QNetwork()
cu.mem('QNetwork controller created')
self.learner = None
self.agent = RegionFilteringAgent(self.controller, self.learner)
self.task = RegionFilteringTask(self.environment,
config.get(mode + 'GroundTruth'))
self.experiment = Experiment(self.task, self.agent)
def runEpoch(self, interactions, maxImgs):
img = 0
s = cu.tic()
while img < maxImgs:
self.experiment.doInteractions(interactions)
self.agent.learn()
self.agent.reset()
self.environment.loadNextEpisode()
img += 1
s = cu.toc('Run epoch with ' + str(maxImgs) + ' episodes', s)
def run(self):
if self.mode == 'train':
self.agent.persistMemory = True
self.agent.startReplayMemory(len(self.environment.db.images),
config.geti('trainInteractions'),
config.geti('stateFeatures'))
self.train()
elif self.mode == 'test':
self.agent.persistMemory = False
self.test()
def train(self):
interactions = config.geti('trainInteractions')
minEpsilon = config.getf('minTrainingEpsilon')
epochSize = len(self.environment.db.images) / 2
epsilon = 1.0
self.controller.setEpsilonGreedy(epsilon)
print 'Epoch 0: Exploration'
self.runEpoch(interactions, len(self.environment.db.images))
self.learner = QLearning()
self.agent.learner = self.learner
epoch = 1
egEpochs = config.geti('epsilonGreedyEpochs')
while epoch <= egEpochs:
epsilon = epsilon - (1.0 - minEpsilon) / float(egEpochs)
if epsilon < minEpsilon: epsilon = minEpsilon
self.controller.setEpsilonGreedy(epsilon)
print 'Epoch', epoch, '(epsilon-greedy:{:5.3f})'.format(epsilon)
self.runEpoch(interactions, epochSize)
epoch += 1
epoch = 1
maxEpochs = config.geti('exploitLearningEpochs')
while epoch <= maxEpochs:
print 'Epoch', epoch + egEpochs, '(exploitation mode: epsilon={:5.3f})'.format(
epsilon)
self.runEpoch(interactions, epochSize)
epoch += 1
def test(self):
interactions = config.geti('testInteractions')
self.controller.setEpsilonGreedy(config.getf('testEpsilon'))
self.runEpoch(interactions, len(self.environment.db.images))
class ReinforcementLearningRunner():
def __init__(self, mode):
self.mode = mode
cu.mem('Reinforcement Learning Started')
self.environment = RegionFilteringEnvironment(config.get(mode+'Database'), mode)
self.controller = QNetwork()
cu.mem('QNetwork controller created')
self.learner = None
self.agent = RegionFilteringAgent(self.controller, self.learner)
self.task = RegionFilteringTask(self.environment, config.get(mode+'GroundTruth'))
self.experiment = Experiment(self.task, self.agent)
def runEpoch(self, interactions, maxImgs):
img = 0
s = cu.tic()
while img < maxImgs:
self.experiment.doInteractions(interactions)
self.agent.learn()
self.agent.reset()
self.environment.loadNextEpisode()
img += 1
s = cu.toc('Run epoch with ' + str(maxImgs) + ' episodes', s)
def run(self):
if self.mode == 'train':
self.agent.persistMemory = True
self.agent.startReplayMemory(len(self.environment.db.images), config.geti('trainInteractions'), config.geti('stateFeatures'))
self.train()
elif self.mode == 'test':
self.agent.persistMemory = False
self.test()
def train(self):
interactions = config.geti('trainInteractions')
minEpsilon = config.getf('minTrainingEpsilon')
epochSize = len(self.environment.db.images)/2
epsilon = 1.0
self.controller.setEpsilonGreedy(epsilon)
print 'Epoch 0: Exploration'
self.runEpoch(interactions, len(self.environment.db.images))
self.learner = QLearning()
self.agent.learner = self.learner
epoch = 1
egEpochs = config.geti('epsilonGreedyEpochs')
while epoch <= egEpochs:
epsilon = epsilon - (1.0-minEpsilon)/float(egEpochs)
if epsilon < minEpsilon: epsilon = minEpsilon
self.controller.setEpsilonGreedy(epsilon)
print 'Epoch',epoch ,'(epsilon-greedy:{:5.3f})'.format(epsilon)
self.runEpoch(interactions, epochSize)
epoch += 1
epoch = 1
maxEpochs = config.geti('exploitLearningEpochs')
while epoch <= maxEpochs:
print 'Epoch',epoch+egEpochs,'(exploitation mode: epsilon={:5.3f})'.format(epsilon)
self.runEpoch(interactions, epochSize)
epoch += 1
def test(self):
interactions = config.geti('testInteractions')
self.controller.setEpsilonGreedy(config.getf('testEpsilon'))
self.runEpoch(interactions, len(self.environment.db.images))
