def main(args):
if args.save_path is not None and not os.path.exists(args.save_path):
os.makedirs(args.save_path)
tf.reset_default_graph()
# global_ep = tf.Variable(
# 0, dtype=tf.int32, name='global_ep', trainable=False)
env = Doom(visiable=True)
global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global')
network = Net(env.state_dim, env.action_dim, 'global', None)
saver = tf.train.Saver()
# sys.exit('..................')
with tf.Session() as sess:
if args.model_path is not None:
print('Loading model...')
ckpt = tf.train.get_checkpoint_state(args.model_path)
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sys.exit('No model path found...')
# print_net_params_number()
while True:
play_episodes(sess, env, network)
def main(args):
if args.save_path is not None and not os.path.exists(args.save_path):
os.makedirs(args.save_path)
tf.reset_default_graph()
global_ep = tf.Variable(0,
dtype=tf.int32,
name='global_ep',
trainable=False)
env = Doom(visiable=False)
Net(env.state_dim, env.action_dim, 'global', None)
num_workers = args.parallel
workers = []
# create workers
for i in range(num_workers):
w = Worker(i, Doom(), global_ep, args)
workers.append(w)
print('%d workers in total.\n' % num_workers)
saver = tf.train.Saver(max_to_keep=3)
with tf.Session() as sess:
coord = tf.train.Coordinator()
if args.model_path is not None:
print('Loading model...')
ckpt = tf.train.get_checkpoint_state(args.model_path)
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print('Initializing a new model...')
sess.run(tf.global_variables_initializer())
print_net_params_number()
# Start work process for each worker in a separated thread
worker_threads = []
for w in workers:
run_fn = lambda: w.run(sess, coord, saver)
t = threading.Thread(target=run_fn)
t.start()
time.sleep(0.5)
worker_threads.append(t)
coord.join(worker_threads)