def __init__(self, event_queue_name, hub_queue_name):
super().__init__()
# create environment
self.conn = boto.sqs.connect_to_region(constants.REGION)
self.event_queue = self.conn.get_queue(event_queue_name)
self.event_queue.set_message_class(MHMessage)
self.env = DogEnv(DogEnv.ALL_QUIET, DogEnv.ALL_QUIET, self.event_queue, hub_queue_name)
self.env.delay = (self.episodes == 1)
# create task
self.task = QuietDogTask(self.env)
# create value table and initialize with ones
# TODO: Get number of states from DogEnv
self.table = ActionValueTable(2*5*4, 5*4)
self.table.initialize(1.)
# create agent with controller and learner - use SARSA(), Q() or QLambda() here
self.learner = SARSA()
# standard exploration is e-greedy, but a different type can be chosen as well
self.learner.explorer = BoltzmannExplorer()
# create agent
self.agent = DogAgent(self.table, self.learner)
# create experiment
self.experiment = Experiment(self.task, self.agent)
class RlOp(threading.Thread):
episodes = 1
epilen = 200
def __init__(self, event_queue_name, hub_queue_name):
super().__init__()
# create environment
self.conn = boto.sqs.connect_to_region(constants.REGION)
self.event_queue = self.conn.get_queue(event_queue_name)
self.event_queue.set_message_class(MHMessage)
self.env = DogEnv(DogEnv.ALL_QUIET, DogEnv.ALL_QUIET, self.event_queue, hub_queue_name)
self.env.delay = (self.episodes == 1)
# create task
self.task = QuietDogTask(self.env)
# create value table and initialize with ones
# TODO: Get number of states from DogEnv
self.table = ActionValueTable(2*5*4, 5*4)
self.table.initialize(1.)
# create agent with controller and learner - use SARSA(), Q() or QLambda() here
self.learner = SARSA()
# standard exploration is e-greedy, but a different type can be chosen as well
self.learner.explorer = BoltzmannExplorer()
# create agent
self.agent = DogAgent(self.table, self.learner)
# create experiment
self.experiment = Experiment(self.task, self.agent)
def run(self):
self.call_run()
def call_run(self):
print('RlOp: running')
# prepare plotting
pylab.gray()
pylab.ion()
for i in range(1000):
# interact with the environment (here in batch mode)
self.experiment.doInteractions(100)
self.agent.learn()
self.agent.reset()
results0 = self.table.params.reshape(2, 4, 5, 20)[0]
results1 = self.table.params.reshape(2, 4, 5, 20)[1]
pp.pprint(results0.argmax(2))
pp.pprint(results1.argmax(2))
# and draw the table
#ar=self.table.params.reshape(2,5,4,5,4)
#for state1 in range(len(constants.SOUNDS)):
# for state2 in range(4):
# pylab.pcolor(ar[1][state1][state2])
# pylab.draw()
results0 = self.table.params.reshape(2, 4, 5, 20)[0]
results1 = self.table.params.reshape(2, 4, 5, 20)[1]
while True:
time.sleep(60)
pp.pprint(results0.argmax(2))
pp.pprint(results1.argmax(2))
