def create_function_nn(sim_config):
function_config = dict()
function_path = sim_config['Function_path']
path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
function_path)
with open(path + '/agent_config.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
funcion_config[row[0]] = row[1]
func_config['min_epsilon'] = 0
func_config['epsilon_decaying_states'] = 0
func_config['min_D_size'] = 0
input_data = function_config['input_data']
num_history = int(function_config['agent_history'])
num_peer = int(sim_config['num_peer'])
if input_data == 'upload' or input_data == 'download':
width = num_history
height = num_peer - 1
elif input_data == 'upload_and_download':
width = num_history
height = (num_peer - 1) * 2
func_config['width'] = width
func_config['height'] = height
neural_network = DeepQNetwork(width, height,
int(func_config['fc2_outputs']),
function_config)
sess = tf.InteractiveSession()
sess.run(tf.global_variabels_initializer())
neural_network.restore_parameters(
sess,
os.path.join(
path, 'model', 'train_network',
'train_network-' + str(sim_config['Function_restore_checkpoint'])))
return neural_network, sess, func_config
class DQNAgent(Peer):
def __init__(self,
sim_config,
agent_config,
ID=0,
strategy='Agent',
training_flag=True):
# parameters
super().__init__(sim_config, ID, strategy)
self.num_history = agent_config['agent_history']
self.training_flag = training_flag
## エージェントの行動の集合
self.enable_actions = list([i] for i in range(self.num_peer)
if i != self.ID)
self.enable_actions.insert(0, [])
self.num_actions = self.num_peer
## 各パラメータの値を格納(詳細はtraining_config.pyに記載)
self.minibatch_size = int(agent_config['minibatch_size'])
self.learning_rate = float(agent_config['learning_rate'])
self.discount_factor = float(agent_config['discount_factor'])
self.max_D_size = int(agent_config['max_D_size'])
self.min_D_size = int(agent_config['min_D_size'])
self.network_update_frequency = int(
agent_config['network_update_frequency'])
self.epsilon_decaying_states = int(
agent_config['epsilon_decaying_states'])
self.min_epsilon = float(agent_config['min_epsilon'])
self.reward_config = str(agent_config['reward_config'])
self.momentum = float(agent_config['momentum'])
self.opt_epsilon = float(agent_config['opt_epsilon'])
# replay memory
self.D = deque(maxlen=self.max_D_size)
# variables
self.current_loss = 0.0
self.current_Q_max = 0.0
self.num_total_states = 0
self.Q_max = 0
self.action_t = []
self.reward_t = [0]
self.action_t_past = 0
# model
self.graph = tf.Graph()
with self.graph.as_default():
#input_layer
self.input_data = agent_config['input_data']
if self.input_data == 'upload' or self.input_data == 'download':
self.width = self.num_history
self.height = self.num_peer - 1
elif self.input_data == 'upload_and_download':
self.width = self.num_history
self.height = (self.num_peer - 1) * 2
self.num_channels = int(agent_config['num_channels'])
# train_network
self.tf_train_input = tf.placeholder(
tf.float32,
shape=(self.minibatch_size, self.height, self.width,
self.num_channels))
self.tf_train_target = tf.placeholder(tf.float32,
shape=(self.minibatch_size,
self.num_actions))
self.tf_filter_input = tf.placeholder(tf.float32,
shape=(self.minibatch_size,
self.num_actions))
self.train_network = DeepQNetwork(self.width, self.height,
self.num_actions, agent_config)
###
#self.train_q_values = self.train_network.q_values(self.tf_train_input)
# target_network
self.tf_target_input = tf.placeholder(
tf.float32,
shape=(self.minibatch_size, self.height, self.width,
self.num_channels))
#self.tf_target_input = tf.placeholder(tf.float32, shape=(1, self.height, self.width, self.num_channels))
#self.tf_target_input = tf.placeholder(tf.float32, [self.minibatch_size, width, height])
self.target_network = DeepQNetwork(self.width, self.height,
self.num_actions, agent_config)
self.target_q_values = self.target_network.q_values(
self.tf_target_input)
# アクション選択用のプレースホルダー
self.tf_action_selection_input = tf.placeholder(
tf.float32,
shape=(1, self.height, self.width, self.num_channels))
self.action_q_values = self.train_network.q_values(
self.tf_action_selection_input)
# loss function
self.loss = self.train_network.clipped_loss(
self.tf_train_input, self.tf_train_target,
self.tf_filter_input)
# optimizer
self.optimizer = tf.train.RMSPropOptimizer(
self.learning_rate,
momentum=self.momentum,
epsilon=self.opt_epsilon,
name='RMSProp')
self.training = self.optimizer.minimize(self.loss, name='training')
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.update_target_network()
def update_target_network(self):
self.train_network.copy_network_to(self.target_network, self.sess)
def select_random_action(self, neighbor_leecher_list, unchoke_num):
unchoke_num = self.calculate_unchoke_num(unchoke_num)
return random.sample(neighbor_leecher_list, unchoke_num)
def get_q_max(self):
if self.num_history > len(self.download_history):
return 0
else:
state = self.get_cur_history()
state = np.reshape(state,
[1, self.height, self.width, self.num_channels])
q_values = self.action_q_values.eval(
session=self.sess,
feed_dict={self.tf_action_selection_input: state})[0]
return np.max(q_values)
def select_greedy_action(self, state, neighbor_leecher_list):
q_values = self.action_q_values.eval(
session=self.sess,
feed_dict={self.tf_action_selection_input: state})[0]
q_index = np.argsort(q_values)[::-1]
for q in q_index:
if self.enable_actions[q] == []:
return []
elif self.enable_actions[q][0] in neighbor_leecher_list:
return self.enable_actions[q]
#self.action_t = self.enable_actions[q_index[0]]
# action_t = []
# for i in q_index:
# if i in neighbor_leecher_list:
# action_t.append(self.enable_actions[i])
# if len(action_t) == self.num_unchoke:
# return action_t
# elif i == self.ID:
# [action_t.append(self.enable_actions[i])
# return action_t
def upload(self, status_list):
self.enable_actions = list([i] for i in range(self.num_actions)
if i != self.ID)
self.enable_actions.insert(0, [])
#print(self.enable_actions)
epsilon = self.calculate_epsilon()
neighbor_leecher_list = [
i for i in range(len(status_list))
if status_list[i] != 'Seeder' and i != self.ID
]
self.unchoke_probability = 1 - (1 / (len(neighbor_leecher_list) + 1))
unchoke_num = min(self.num_unchoke, len(neighbor_leecher_list))
### 隣接リーチャがいない時
if neighbor_leecher_list == []:
self.action_t = []
return self.action_t
### 十分なデータが集まっていない時
elif len(self.download_history
) <= self.num_history or not self.has_enough_memory():
self.action_t = self.select_random_action(neighbor_leecher_list,
unchoke_num)
return self.action_t
else:
### イプシロンの確率でランダム戦略
if random.random() <= epsilon:
self.action_t = self.select_random_action(
neighbor_leecher_list, unchoke_num)
return self.action_t
else:
### 直近の履歴情報を返す
state = self.get_cur_history()
state = np.reshape(
state, [1, self.height, self.width, self.num_channels])
### Q値によって選択
self.action_t = self.select_greedy_action(
state, neighbor_leecher_list)
return self.action_t
def store_experience(self, state, action, reward, state_1, terminal):
self.D.append((state, action, reward, state_1, terminal))
def experience_replay(self):
state_minibatch = []
action_minibatch = []
reward_minibatch = []
state_1_minibatch = []
terminal_minibatch = []
# sample random minibatch
minibatch_size = min(len(self.D), self.minibatch_size)
minibatch_indexes = np.random.randint(0, len(self.D), minibatch_size)
for j in minibatch_indexes:
state_j, action_j, reward_j, state_j_1, terminal = self.D[j]
action_j_index = action_j
state_minibatch.append(state_j)
action_minibatch.append(action_j)
reward_minibatch.append(reward_j)
state_1_minibatch.append(state_j_1)
terminal_minibatch.append(terminal)
state_minibatch = np.reshape(
state_minibatch,
[self.minibatch_size, self.height, self.width, self.num_channels])
state_1_minibatch = np.reshape(
state_1_minibatch,
[self.minibatch_size, self.height, self.width, self.num_channels])
### target_networkでQ値を算出(教師データ)
### train_q_valuesはtarget_networkを使って学習を行わない(2013年版DQN)
#target_qs = self.train_q_values.eval(feed_dict={self.tf_train_input: state_1_minibatch})
target_qs = self.target_q_values.eval(
session=self.sess,
feed_dict={self.tf_target_input: state_1_minibatch})
target = np.zeros(shape=(self.minibatch_size, self.num_actions),
dtype=np.float32)
q_value_filter = np.zeros(shape=(self.minibatch_size,
self.num_actions),
dtype=np.float32)
for i in range(self.minibatch_size):
terminal = terminal_minibatch[i]
action_index = action_minibatch[i]
#print(action_index)
reward = reward_minibatch[i]
target[i][
action_index] = reward if terminal else reward + self.learning_rate * np.max(
target_qs[i])
q_value_filter[i][action_index] = 1.0
_, self.current_loss = self.sess.run(
[self.training, self.loss],
feed_dict={
self.tf_train_input: state_minibatch,
self.tf_train_target: target,
self.tf_filter_input: q_value_filter
})
def load_model(self, file_path):
self.train_network.restore_parameters(self.sess, file_path)
### CNNのモデルを保存
def save_model(self, save_path, num_episode):
if not os.path.exists(os.path.join(save_path, 'model',
'train_network')):
os.makedirs(os.path.join(save_path, 'model', 'train_network'))
if not os.path.exists(
os.path.join(save_path, 'model', 'target_network')):
os.makedirs(os.path.join(save_path, 'model', 'target_network'))
self.train_network.save_parameters(
self.sess, save_path + '/model/train_network/train_network',
num_episode)
self.target_network.save_parameters(
self.sess, save_path + '/model/target_network/train_network',
num_episode)
def get_cur_history(self):
len_his = len(self.download_history)
cur_up = copy.deepcopy(self.upload_history[len_his -
self.num_history:])
cur_up = delete_row(cur_up, self.ID)
cur_down = copy.deepcopy(self.download_history[len_his -
self.num_history:])
cur_down = delete_row(cur_down, self.ID)
if self.input_data == 'upload':
return np.transpose(cur_up)
elif self.input_data == 'download':
return np.transpose(cur_down)
elif self.input_data == 'upload_and_download':
up_down = np.r_[cur_up, cur_down]
up_down = up_down.transpose()
if np.shape(up_down) == (self.num_peer - 1, self.num_history * 2):
#print('ok ',np.shape(up_down))
up_down = up_down.reshape((self.num_peer - 1) * 2,
self.num_history)
else:
#print('no ', np.shape(up_down))
up_down = None
return up_down
def calculate_epsilon(self):
if self.epsilon_decaying_states == 0:
return self.min_epsilon
else:
return max(
self.min_epsilon,
1.0 - (self.num_total_states / self.epsilon_decaying_states))
def has_enough_memory(self):
return len(self.D) >= self.min_D_size
def next_step(self):
if self.num_history < len(
self.download_history) and self.training_flag:
self.num_total_states += 1
#時刻tの状態, 行動, 報酬
state_t = self.state_t_past
action_t = self.action_t_past
state_t_1 = self.get_cur_history()
#現在の状態を保存しておき, 次のステップで用いる
self.state_t_past = state_t_1
self.action_t_past = self.action_t
self.reward_t = []
if self.reward_config == 'all_download':
self.reward_t.append(sum(self.current_download))
elif self.reward_config == 'each_download':
for a_t in action_t:
self.reward_t.append(self.current_download[a_t])
elif self.reward_config == 'each_download_penalty':
for a_t in action_t:
each_download = self.current_download[a_t]
if a_t == self.ID:
self.reward_t.append(0)
elif each_download >= 1:
self.reward_t.append(1)
else:
self.reward_t.append(-1)
reward_t = self.reward_t
#状態を更新
super().next_step()
terminal = self.is_seeder()
## 行動とアクションを再生メモリに格納
self.store_experience(state_t,
[self.enable_actions.index(action_t)],
reward_t, state_t_1, terminal)
## 十分なメモリが揃ったら学習を開始
if self.has_enough_memory():
if (self.num_total_states %
self.network_update_frequency) == 0:
print('-----update_network------')
self.update_target_network()
self.experience_replay()
else:
self.state_t_past = self.get_cur_history()
self.action_t_past = self.action_t
super().next_step()
### オブジェクトをリセットしないため, このメソッドを呼び出して次のエピソードへ移行する.
def reset_history(self):
#持っているピース.デフォルトは0
self.have_piece = 0
#隣接ピアへのアップロード履歴を保存する変数(多分0か1しか検討しなさそうやけど)
self.current_upload = [0 for i in range(self.num_peer)]
#隣接ピアからの直近のダウンロード履歴を保存する変数
#初期はすべて1を入れてランダムに行うようにする(0入れて一番古いのを1にするのもあり)
self.current_download = [0 for i in range(self.num_peer)]
#隣接ピアからのダウンロード履歴を保存する変数
self.upload_history = [[0 for i in range(self.num_peer)]]
#隣接ピアからのアップロード履歴を保存する変数
self.download_history = [[1 for i in range(self.num_peer)]]
self.strategy = 'Agent'
