def train():
env = wlanEnv(CONTROLLER_IP, BUFFER_LEN, timeInterval=ENV_REFRESH_INTERVAL)
env.start()
numAPs, numActions, numAdditionDim = env.getDimSpace()
brain = BrainDQN(numActions,
numAPs,
numAdditionDim,
BUFFER_LEN,
param_file='saved_networks/network-dqn.params')
while not env.observe()[0]:
time.sleep(0.5)
observation0 = env.observe()[1]
brain.setInitState(observation0)
np.set_printoptions(threshold=5)
print 'Initial observation:\n' + str(observation0)
data = {}
fig = Display(env.id2ap)
fig.display()
try:
while True:
action, q = brain.getAction()
print 'action:\n' + str(action.argmax())
reward, throught, nextObservation = env.step(action)
print 'reward: ' + str(reward) + ', throught: ' + str(throught)
print 'Next observation:\n' + str(nextObservation)
data['timestamp'] = time.time()
data['rssi'] = nextObservation[-1]
data['q'] = q
data['reward'] = reward
data['action_index'] = np.argmax(action)
fig.append(data)
brain.setPerception(nextObservation, action, reward, False)
except KeyboardInterrupt:
print 'Saving replayMemory......'
brain.saveReplayMemory()
fig.stop()
pass
def test():
env = wlanEnv(CONTROLLER_IP,
BUFFER_LEN,
timeInterval=ENV_REFRESH_INTERVAL,
no_guarantee=True)
env.start()
numAPs, numActions, numAdditionDim = env.getDimSpace()
brain = BrainDQN(numActions,
numAPs,
numAdditionDim,
BUFFER_LEN,
param_file='saved_networks/network-dqn.params')
while not env.observe()[0]:
time.sleep(0.5)
observation = env.observe()[1]
np.set_printoptions(threshold=5)
data = {}
fig = Display(env.id2ap, PREDICT=True)
fig.display()
try:
while True:
action, q_value, action_index, feature_vector = brain.predict(
observation)
print 'action:\n' + str(action_index)
reward, throught, observation = env.step(action)
print 'q_value: ' + str(q_value)
print 'reward: ' + str(reward) + ', throught: ' + str(throught)
data['timestamp'] = time.time()
data['rssi'] = observation[-1]
data['q'] = q_value
data['reward'] = reward
data['action_index'] = action_index
data['feature_vector'] = feature_vector
fig.append(data)
print 'Next observation:\n' + str(observation)
time.sleep(2)
except KeyboardInterrupt:
fig.stop()
