def _create_graph(self):
if self.reuse:
tf.get_variable_scope().reuse_variables()
assert tf.get_variable_scope().reuse
worker_device = "/job:worker/task:%d" % self.index + self.device
print("worker_device:", worker_device)
with tf.device(tf.train.replica_device_setter(worker_device=worker_device, cluster=self.cluster)):
self.results_sum = tf.get_variable(trainable=False, name="results_sum", shape=[], initializer=tf.zeros_initializer)
self.game_num = tf.get_variable(trainable=False, name="game_num", shape=[], initializer=tf.zeros_initializer)
self.global_steps = tf.get_variable(trainable=False, name="global_steps", shape=[], initializer=tf.zeros_initializer)
self.mean_win_rate = tf.summary.scalar('mean_win_rate_dis', self.results_sum / self.game_num)
self.merged = tf.summary.merge([self.mean_win_rate])
self.dynamic_net = DynamicNetwork('train', self.sess, load_path=self.dynamic_load_path, save_path=self.dynamic_save_path)
scope = "PolicyNN"
with tf.variable_scope(scope):
ob_space = C._SIZE_SIMPLE_INPUT
act_space_array = C._SIZE_MAX_ACTIONS
self.policy = Policy_net('policy', self.sess, ob_space, act_space_array)
self.policy_old = Policy_net('old_policy', self.sess, ob_space, act_space_array)
self.policy_ppo = PPOTrain('PPO', self.sess, self.policy, self.policy_old, epoch_num=P.src_epoch_num)
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
self.policy_saver = tf.train.Saver(var_list=var_list)
def _create_graph(self):
if self.reuse:
tf.get_variable_scope().reuse_variables()
assert tf.get_variable_scope().reuse
worker_device = "/job:worker/task:%d" % self.index + self.device
with tf.device(tf.train.replica_device_setter(worker_device=worker_device, cluster=self.cluster)):
self.results_sum = tf.get_variable(name="results_sum", shape=[], initializer=tf.zeros_initializer)
self.game_num = tf.get_variable(name="game_num", shape=[], initializer=tf.zeros_initializer)
self.global_steps = tf.get_variable(name="global_steps", shape=[], initializer=tf.zeros_initializer)
self.win_rate = self.results_sum / self.game_num
self.mean_win_rate = tf.summary.scalar('mean_win_rate_dis', self.results_sum / self.game_num)
self.merged = tf.summary.merge([self.mean_win_rate])
mini_scope = "MiniPolicyNN"
with tf.variable_scope(mini_scope):
ob_space = _SIZE_MINI_INPUT
act_space_array = _SIZE_MINI_ACTIONS
self.policy = Policy_net('policy', self.sess, ob_space, act_space_array)
self.policy_old = Policy_net('old_policy', self.sess, ob_space, act_space_array)
self.policy_ppo = PPOTrain('PPO', self.sess, self.policy, self.policy_old, lr=P.mini_lr, epoch_num=P.mini_epoch_num)
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
self.policy_saver = tf.train.Saver(var_list=var_list)