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)
saver = tf.train.Saver(tf.all_variables())
sess.run(tf.initialize_all_variables())
summary_writer = tf.train.SummaryWriter(args.save_folder, sess.graph_def)
if args.restore != "No":
saver.restore(sess, args.save_folder+args.restore)
#just display games
sim.reset()
#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)
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)
saver = tf.train.Saver(tf.all_variables())
sess.run(tf.initialize_all_variables())
summary_writer = tf.train.SummaryWriter(args.save_folder, sess.graph_def)
if args.restore != "No":
saver.restore(sess, args.save_folder+args.restore)
def redraw_heatmap(x,y,angle):
#convert to radians
angle = (angle - 90) *numpy.pi / 180.0
#load the screen
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)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads_and_vars = optimizer.compute_gradients(
learner.single_action_cost
) #could also use learner.normal_cost
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
loss_summary = tf.scalar_summary("cost",
learner.single_action_cost)
#visualize those first level filters
#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)
saver = tf.train.Saver(tf.all_variables())
sess.run(tf.initialize_all_variables())
summary_writer = tf.train.SummaryWriter(args.save_folder,
sess.graph_def)
if args.restore != "No":
saver.restore(sess, args.save_folder + args.restore)
#just display games
sim.reset()
previous_state = numpy.zeros((sim.image_size, sim.image_size, 3))
