args = parser.parse_args()
image_size = args.image_size
number_of_games = args.number_of_games
batch_size = args.batch_size
gpu_flag = args.gpu
learning_rate = args.learning_rate
#epsilon is the decision parameter - do you use the actor's actions or do them randomly?
#initially, you want to use random actions - but over time as the actor learns,
#the actor's actions will be better
epsilon = 1
epsilon_decay = args.epsilon_decay
display_steps = args.display_iterations
sim = Simulator(1)
if gpu_flag > -1:
device_string = '/gpu:{}'.format(gpu_flag)
else:
device_string = "/cpu:0"
with tf.Graph().as_default(), tf.device(device_string):
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False))
with sess.as_default():
learner = ActionLearner(
image_size=sim.image_size,
n_filters=args.number_of_filters,
n_hidden=args.number_of_hidden,
n_out=sim.number_of_actions
)
# # #this will do it linearly
# # previous_state[0][1] += -1*reward*np.power(discount_factor,discount_iterator)
# # discount_iterator += 1
#
# break
return score_list #+ left_state_list
class FakeActor:
def __init__(self,num_actions):
self.number_of_actions = num_actions
self.display_output = [0,0]
def return_action(self,simulator_screen):
return np.random.randint(self.number_of_actions)
if __name__ == "__main__":
sim = Simulator(10)
actor = FakeActor(3)
start_time = time.time()
game_state_list = make_states(sim,actor,1,200,10,winners_only=False)
print(time.time() - start_time)
for state_list in game_state_list:
for state in state_list:
print(state[0][1:4],np.mean(state[0][4]))
cv2.imshow('Phong!',cv2.resize(state[0][4],(0,0),fx=2,fy=2))
np.save('phong_screen',state[0][4])
cv2.waitKey(400)
args = parser.parse_args()
image_size = args.image_size
number_of_games = args.number_of_games
batch_size = args.batch_size
gpu_flag = args.gpu
learning_rate = args.learning_rate
#epsilon is the decision parameter - do you use the actor's actions or do them randomly?
#initially, you want to use random actions - but over time as the actor learns,
#the actor's actions will be better
epsilon = 1
epsilon_decay = args.epsilon_decay
display_steps = args.display_iterations
sim = Simulator(1,screen_size=args.image_size,state_space=args.state_space)
if gpu_flag > -1:
device_string = '/gpu:{}'.format(gpu_flag)
else:
device_string = "/cpu:0"
with tf.Graph().as_default(), tf.device(device_string):
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False))
with sess.as_default():
learner = ActionLearner(
image_size=sim.screen_size,
n_filters=args.number_of_filters,
n_hidden=args.number_of_hidden,
n_out=sim.number_of_actions
)
args = parser.parse_args()
image_size = args.image_size
number_of_games = args.number_of_games
batch_size = args.batch_size
gpu_flag = args.gpu
learning_rate = args.learning_rate
#epsilon is the decision parameter - do you use the actor's actions or do them randomly?
#initially, you want to use random actions - but over time as the actor learns,
#the actor's actions will be better
epsilon = 1
epsilon_decay = args.epsilon_decay
display_steps = args.display_iterations
sim = Simulator(1,
screen_size=args.image_size,
state_space=args.state_space)
if gpu_flag > -1:
device_string = '/gpu:{}'.format(gpu_flag)
else:
device_string = "/cpu:0"
with tf.Graph().as_default(), tf.device(device_string):
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
with sess.as_default():
learner = ActionLearner(image_size=sim.screen_size,
n_filters=args.number_of_filters,
n_hidden=args.number_of_hidden,
n_out=sim.number_of_actions)
learner.set_sess(sess)
args = parser.parse_args()
image_size = args.image_size
number_of_games = args.number_of_games
batch_size = args.batch_size
gpu_flag = args.gpu
learning_rate = args.learning_rate
#epsilon is the decision parameter - do you use the actor's actions or do them randomly?
#initially, you want to use random actions - but over time as the actor learns,
#the actor's actions will be better
epsilon = 1
epsilon_decay = args.epsilon_decay
display_steps = args.display_iterations
sim = Simulator(1)
if gpu_flag > -1:
device_string = '/gpu:{}'.format(gpu_flag)
else:
device_string = "/cpu:0"
with tf.Graph().as_default(), tf.device(device_string):
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
with sess.as_default():
learner = ActionLearner(image_size=sim.image_size,
n_filters=args.number_of_filters,
n_hidden=args.number_of_hidden,
n_out=sim.number_of_actions)
learner.set_sess(sess)
global_step = tf.Variable(0, name="global_step", trainable=False)
