def __init__(self):
#Setting the training parameters
self.batch_size = 4 #How many experience traces to use for each training step.
self.trace_length = 8 #How long each experience trace will be when training
self.update_freq = 5 #How often to perform a training step.
self.y = .99 #Discount factor on the target Q-values
self.startE = 1 #Starting chance of random action
self.endE = 0.1 #Final chance of random action
self.anneling_steps = 10000 #How many steps of training to reduce startE to endE.
self.num_episodes = 10000 #How many episodes of game environment to train network with.
self.pre_train_steps = 10000 #How many steps of random actions before training begins.
self.load_model = True #Whether to load a saved model.
self.path = "./drqn" #The path to save our model to.
self.h_size = 512 #The size of the final convolutional layer before splitting it into Advantage and Value streams.
self.max_epLength = 300 #The max allowed length of our episode.
self.time_per_step = 1 #Length of each step used in gif creation
self.summaryLength = 100 #Number of epidoes to periodically save for analysis
self.tau = 0.001
# for Tracking
self.cap = None
self.col = -1
self.width = -1
self.row = -1
self.height = -1
self.frame = None
self.frame2 = None
self.inputmode = False
self.rectangle = False
self.trackWindow = None
self.roi_hist = None
self.roi = None
self.caffe_model_path = './MobileNetSSD_deploy.caffemodel'
self.prorotxt_path = './MobileNetSSD_deploy.prototxt.txt'
self.net = None
self.obstacle_points = []
self.target_point = None
self.obstacle_box_color = (0, 0, 255)
self.tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN'
]
self.tracker_type = self.tracker_types[2]
if self.tracker_type == 'BOOSTING':
self.tracker = cv2.TrackerBoosting_create()
if self.tracker_type == 'MIL':
self.tracker = cv2.TrackerMIL_create()
if self.tracker_type == 'KCF':
self.tracker = cv2.TrackerKCF_create()
if self.tracker_type == 'TLD':
self.tracker = cv2.TrackerTLD_create()
else:
self.tracker = cv2.TrackerMedianFlow_create()
self.game = sim(200, True)
def __init__(self):
#Setting the training parameters
self.batch_size = 4 #How many experience traces to use for each training step.
self.trace_length = 8 #How long each experience trace will be when training
self.update_freq = 5 #How often to perform a training step.
self.y = .99 #Discount factor on the target Q-values
self.startE = 1 #Starting chance of random action
self.endE = 0.1 #Final chance of random action
self.anneling_steps = 10000 #How many steps of training to reduce startE to endE.
self.num_episodes = 10000 #How many episodes of game environment to train network with.
self.pre_train_steps = 10000 #How many steps of random actions before training begins.
self.load_model = False #Whether to load a saved model.
self.path = "./drqn" #The path to save our model to.
self.h_size = 512 #The size of the final convolutional layer before splitting it into Advantage and Value streams.
self.max_epLength = 300 #The max allowed length of our episode.
self.time_per_step = 1 #Length of each step used in gif creation
self.summaryLength = 100 #Number of epidoes to periodically save for analysis
self.tau = 0.001
self.game = sim(200, True)
def __init__(self, flag):
self.batch_size = 64 #How many experiences to use for each training step.
self.update_freq = 4 #How often to perform a training step.
self.y = .99 #Discount factor on the target Q-values
self.startE = 1 #Starting chance of random action
self.endE = 0.1 #Final chance of random action
self.annealing_steps = 10000. #How many steps of training to reduce startE to endE.
self.num_episodes = 10000 #How many episodes of game environment to train network with.
self.pre_train_steps = 10000 #How many steps of random actions before training begins.
self.max_epLength = 300 #The max allowed length of our episode.
self.load_model = False #Whether to load a saved model.
self.path = "./dqn" #The path to save our model to.
self.h_size = 512 #The size of the final convolutional layer before splitting it into Advantage and Value streams.
self.tau = 0.001 #Rate to update target network toward primary network
self.action_num = 5
tf.reset_default_graph()
self.mainQN = DQN(self.h_size, self.action_num)
self.targetQN = DQN(self.h_size, self.action_num)
self.init = tf.global_variables_initializer()
self.saver = tf.train.Saver()
self.trainables = tf.trainable_variables()
self.targetOps = self.updateTargetGraph(self.trainables, self.tau)
self.myBuffer = experience_buffer()
# Set the rate of random action decrease.
self.e = self.startE
self.stepDrop = (self.startE - self.endE) / self.annealing_steps
# create lists to contain total rewards and steps per episode
self.jList = []
self.rList = []
self.total_steps = 0
self.game = sim(200, True)
self.is_Train = flag
# for Tracking
self.cap = None
self.col = -1
self.width = -1
self.row = -1
self.height = -1
self.frame = None
self.frame2 = None
self.inputmode = False
self.rectangle = False
self.trackWindow = None
self.roi_hist = None
self.roi = None
self.caffe_model_path = './MobileNetSSD_deploy.caffemodel'
self.prorotxt_path = './MobileNetSSD_deploy.prototxt.txt'
self.net = None
self.obstacle_points = []
self.target_point = None
self.obstacle_box_color = (0, 0, 255)
self.tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN'
]
self.tracker_type = self.tracker_types[2]
if self.tracker_type == 'BOOSTING':
self.tracker = cv2.TrackerBoosting_create()
if self.tracker_type == 'MIL':
self.tracker = cv2.TrackerMIL_create()
if self.tracker_type == 'KCF':
self.tracker = cv2.TrackerKCF_create()
if self.tracker_type == 'TLD':
self.tracker = cv2.TrackerTLD_create()
else:
self.tracker = cv2.TrackerMedianFlow_create()