def main():
sys.setrecursionlimit(2000)
config = Configuration()
with open(config.DATA_FOLDER + '/config.txt', 'r') as f:
configFile = f.read().split(',')
print('Parameters', configFile)
config.EPSILON_START = float(configFile[0])
config.LOAD_NET_NUMBER = int(float(configFile[1]))
agentTF = AgentTF(config.STATE_SIZE, config.PHI_LENGTH, config.ACTION_SIZE,
config.HIDDEN_LAYERS, config.BATCH_SIZE, config.TAU,
config.GAMMA)
if config.LOAD_NET_NUMBER > 0:
dataSet = loadDataSet(config.DATA_FOLDER, config.LOAD_NET_NUMBER)
agentTF.restore_model(config.DATA_FOLDER)
countTotalSteps = config.LOAD_NET_NUMBER
else:
# Initialize DataSet
dataSet = DataSet(config.STATE_SIZE, config.REPLAY_MEMORY_SIZE,
config.PHI_LENGTH, config.RNG)
countTotalSteps = 0
openLearningFile(config.DATA_FOLDER)
eC = environmentControl(config.PATH_ROBOT, config.PATH_GOAL,
config.PATH_LAUNCHFILE)
eC.spawn(config.ROBOT_NAME)
eC.spawnGoal()
eC.setRandomModelState(config.ROBOT_NAME)
#eC.pause()
dP = dataProcessor(eC, config.ROBOT_NAME, config.PHI_LENGTH,
config.STATE_SIZE, config.NUM_SENSOR_VAL,
config.SENSOR_RANGE_MAX, config.SENSOR_RANGE_MIN,
config.VEL, config.VEL_CURVE, config.UPDATE_TIME,
config.SPEED_UP)
lastState = np.zeros((1, config.STATE_SIZE))
lastReward = 0
lastAction = 0
countSteps = 0
batchCount = 0
lossAverages = np.empty([0])
epochCount = 0
epsilon = max(config.EPSILON_START, config.EPSILON_MIN)
epsilonRate = config.EPSILON_DECAY
quit = False
try:
for i in range(4):
action = np.random.randint(config.ACTION_SIZE)
dP.action(action)
state, reward = dP.getStateReward()
dataSet.addSample(lastState, action, reward, state, dP.isGoal)
countTotalSteps += 1
countSteps += 1
lastState = state
if config.EPSILON_START < -0:
quit = True
while not quit:
if countTotalSteps % 1000 == 0:
updateLearningFile(config.DATA_FOLDER, lossAverages,
countTotalSteps)
lossAverages = np.empty([0])
print(countTotalSteps)
phi = dataSet.phi(lastState)
action = agentTF.getAction(phi, epsilon)
#action=userAction()
eC.unpause()
dP.action(action)
state, reward = dP.getStateReward()
eC.pause()
if dP.isGoal:
print('The goal was reached in ', countSteps, ' steps')
countSteps = 1
eC.setRandomModelState(config.ROBOT_NAME)
eC.setRandomModelState('goal')
dP.isGoal = False
if dP.flipped:
eC.setRandomModelState(config.ROBOT_NAME)
dP.flipped = False
# After NUM_STEPS the chance is over
if countSteps % config.NUM_STEPS == 0:
countSteps = 1
reward -= 1
eC.setRandomModelState(config.ROBOT_NAME)
eC.setRandomModelState('goal')
print('Your chance is over! Try again ...')
#print(reward)
dataSet.addSample(lastState, action, reward, state, dP.isGoal)
# Training
if countTotalSteps > config.REPLAY_START_SIZE and countTotalSteps % 5 == 0:
batchStates, batchActions, batchRewards, batchNextStates, batchTerminals= \
dataSet.randomBatch(config.BATCH_SIZE)
loss = agentTF.train(batchStates, batchActions, batchRewards,
batchNextStates, batchTerminals)
#print('Loss', loss)
# count How many trainings had been done
batchCount += 1
# add loss to lossAverages
lossAverages = np.append(lossAverages, loss)
#Update Epsilon save dataSet, network
if countTotalSteps % config.SIZE_EPOCH == 0:
# Number of Epochs
epochCount += 1
# Update Epsilon
if (epsilon - epsilonRate) < config.EPSILON_MIN - 0.01:
quit = True
epsilon = max(epsilon - epsilonRate, config.EPSILON_MIN)
print('Epsilon updated to: ', epsilon)
agentTF.save_model(countTotalSteps, config.DATA_FOLDER)
saveDataSet(config.DATA_FOLDER, countTotalSteps, dataSet)
lastState = state
countTotalSteps += 1
countSteps += 1
except rospy.exceptions.ROSException:
agentTF.save_model(countTotalSteps, config.DATA_FOLDER)
saveDataSet(config.DATA_FOLDER, countTotalSteps, dataSet)
agentTF.close()
eC.close()
with open(config.DATA_FOLDER + '/config.txt', 'w') as f:
out = "{},{}".format(epsilon, countTotalSteps)
f.write(out)
state,reward=dP.getStateReward()
action=np.random.randint(config.ACTION_SIZE)
dP.action(action)
dataSet.addSample(state,
action,
reward,
dP.isGoal)
countTotalSteps+=1
countSteps+=1
while not quit:
if countTotalSteps%1000==0:
print(countTotalSteps)
state,reward=dP.getStateReward()
phi=dataSet.phi(state)
#print('phi: ', phi)
action=network.choose_action(phi, epsilon)
#action=np.random.randint(config.ACTION_SIZE)
#time.sleep(0.5)
dP.action(action)
#print('state: ', state)
#print('reward: ', reward)
#print('action: ', action)
# Check every 100 steps if is Flipped and Goal was reached
if countSteps % 20 == 0:
if dP.isGoal:
countSteps = 1
eC.setRandomModelState(config.ROBOT_NAME)
eC.setRandomModelState('goal')
