class DDPG:
"""docstring for DDPG"""
def __init__(self, sess, env, par_idx):
self.name = 'DDPG' # name for uploading results
self.environment = env
# Randomly initialize actor network and critic network
# with both their target networks
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
self.par_idx = par_idx
self.sess = sess
with tf.variable_scope("particle_" + str(par_idx)):
self.actor_network = ActorNetwork(self.sess, self.state_dim,
self.action_dim, self.par_idx)
self.critic_network = CriticNetwork(self.sess, self.state_dim,
self.action_dim)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(self.action_dim)
def train(self):
#print "train step",self.time_step
# Sample a random minibatch of N transitions from replay buffer
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
# Calculate y_batch
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,
next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, state_batch, action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients)
#self.actor_network.train(q_gradient_batch,state_batch)
self.actor_network.save_gradient(q_gradient_batch, state_batch)
def update_target(self):
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def noise_action(self, state):
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.action(state)
return action + self.exploration_noise.noise()
def action(self, state):
action = self.actor_network.action(state)
return action
def perceive(self, state, action, reward, next_state, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state, action, reward, next_state, done)
# Store transitions to replay start size then start training
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.train()
#if self.time_step % 10000 == 0:
#self.actor_network.save_network(self.time_step)
#self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
def save_to_buffer(self, state, action, reward, next_state, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state, action, reward, next_state, done)
if done:
self.exploration_noise.reset()
def can_train(self):
if self.replay_buffer.count() > REPLAY_START_SIZE:
return True
else:
return False
class DDPG:
"""docstring for DDPG"""
def __init__(self, sess, data_fname):
self.name = 'DDPG'
# Randomly initialize actor network and critic network
# with both their target networks
self.name = 'DDPG' # name for uploading results
# Randomly initialize actor network and critic network
# with both their target networks
self.state_dim = Hp.state_dim
self.action_dim = Hp.action_dim
print(self.state_dim, self.action_dim)
self.sess = sess
self.state_input = [
tf.placeholder(tf.float32, shape=(None, None, Hp.n_coord))
for _ in xrange(Hp.categories)
]
#tf.placeholder("float",[None,self.state_dim])
self.target_state_input = [
tf.placeholder(tf.float32, shape=(None, None, Hp.n_coord))
for _ in xrange(Hp.categories)
]
#tf.placeholder("float",[None,self.state_dim])
self.state_network = StateEnc(self.sess, self.state_input,
self.target_state_input)
state_batch = self.state_network.encoding
next_state_batch = self.state_network.target_encoding
weights, biases, w_i2h0, w_h2h0, w_b0, w_i2h1, w_h2h1, w_b1, w_i2h2, w_h2h2, w_b2 = self.state_network.get_parameters(
)
state_network_params = weights + biases + [
w_i2h0, w_h2h0, w_b0, w_i2h1, w_h2h1, w_b1, w_i2h2, w_h2h2, w_b2
]
self.actor_network = ActorNetwork(self.sess, Hp.n_hidden,
self.action_dim, self.state_input,
state_batch, next_state_batch,
state_network_params)
self.critic_network = CriticNetwork(self.sess, Hp.n_hidden,
self.action_dim, state_batch,
next_state_batch)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(Hp.REPLAY_BUFFER_SIZE, data_fname)
self.summary_str2 = None
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(self.action_dim)
def train(self):
#print "train step",self.time_step
# Sample a random minibatch of N transitions from replay buffer
minibatches = self.replay_buffer.get_batch(Hp.batch_size * Hp.N_TRAIN)
print("######### TRAINING #############")
for k in range(Hp.N_TRAIN):
minibatch = minibatches[k * Hp.batch_size:(k + 1) * Hp.batch_size]
state_batch_r = np.asarray([data[0] for data in minibatch])
state_batch = []
for j in range(Hp.categories):
new_cat = np.stack(state_batch_r[:, j], axis=0)
state_batch.append(new_cat)
#state_batch = [np.expand_dims(state_batch, axis=1)]
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch_r = np.asarray([data[3] for data in minibatch])
next_state_batch = []
for j in range(Hp.categories):
new_cat = np.stack(next_state_batch_r[:, j], axis=0)
next_state_batch.append(new_cat)
#next_state_batch = [np.expand_dims(next_state_batch, axis=1)]
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch,
[Hp.batch_size, self.action_dim])
next_action_batch = self.actor_network.target_actions(
self.target_state_input, next_state_batch)
q_value_batch = self.critic_network.target_q(
self.target_state_input, next_state_batch, next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] +
Hp.GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [Hp.batch_size, 1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, self.state_input, state_batch,
action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(
self.state_input, state_batch)
q_gradient_batch = self.critic_network.gradients(
self.state_input, state_batch, action_batch_for_gradients)
self.summary_str2 = self.actor_network.train(
q_gradient_batch, self.state_input, state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
self.state_network.update_target()
def noise_action(self, state):
# Select action a_t according to the current policy and exploration noise
state = [np.expand_dims(el, axis=0) for el in state]
action = self.actor_network.action(state)
print("no noise ", action)
return np.clip(
action +
self.exploration_noise.noise() * np.array([-17.0, 17.0, 900.0]),
[-35.0, 0.0, 0.0], [0.0, 35.0, 2000.0])
def action(self, state):
state = [np.expand_dims(el, axis=0) for el in state]
action = self.actor_network.action(state)
return action
def perceive(self, state, action, reward, next_state, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state, action, reward, next_state, done)
# Store transitions to replay start size then start training
if self.replay_buffer.count() > Hp.REPLAY_START_SIZE:
self.train()
#if self.time_step % 10000 == 0:
#self.actor_network.save_network(self.time_step)
#self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
class DDPG:
"""docstring for DDPG"""
def __init__(self, state_dim, action_dim, env):
self.name = 'DDPG' # name for uploading results
self.time_step = 0
self.state_dim = state_dim
self.action_dim = action_dim
self.environment = env
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess, self.state_dim,
self.action_dim)
self.critic_network = CriticNetwork(self.sess, self.state_dim,
self.action_dim)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(self.action_dim)
def train(self):
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
# Calculate y_batch
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,
next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, state_batch, action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients)
self.actor_network.train(q_gradient_batch, state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def noise_action(self, state):
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.action(state)
return action + self.exploration_noise.noise()
def action(self, state):
action = self.actor_network.action(state)
return action
def perceive(self, state, action, reward, next_state, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state, action, reward, next_state, done)
if self.replay_buffer.count() == REPLAY_START_SIZE:
print('\n---------------Start training---------------')
# Store transitions to replay start size then start training
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.time_step += 1
self.train()
if self.time_step % 10000 == 0 and self.time_step > 0:
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
return self.time_step
class DDPG:
def __init__(self, env, state_dim, action_dim):
self.name = 'DDPG'
self.environment = env
self.time_step = 0
self.state_dim = state_dim
self.action_dim = action_dim
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess, self.state_dim,
self.action_dim)
self.critic_network = CriticNetwork(self.sess, self.state_dim,
self.action_dim)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
def train(self):
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,
next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, state_batch, action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients)
self.actor_network.train(q_gradient_batch, state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def action(self, state):
action = self.actor_network.action(state)
return action
def perceive(self, state, action, reward, next_state, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state, action, reward, next_state, done)
if self.replay_buffer.count() == REPLAY_START_SIZE:
print('\n---------------Start training---------------')
# Store transitions to replay start size then start training
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.time_step += 1
self.train()
if self.time_step % 10000 == 0 and self.time_step > 0:
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
return self.time_step
class DDPG:
def __init__(self, env):
self.name = 'DDPG'
self.environment = env
self.episode = 0
self.epsilon = 0.98
self.one_number = 1
self.mean = []
self.state_dim = len(obs2state(env.reset().observation))
self.action_dim = env.action_spec().shape[0]
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess, self.state_dim,
self.action_dim)
self.critic_network = CriticNetwork(self.sess, self.state_dim,
self.action_dim)
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
self.exploration_noise = OUNoise(self.action_dim)
def train(self):
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,
next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
self.critic_network.train(y_batch, state_batch, action_batch)
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients)
self.actor_network.train(q_gradient_batch, state_batch)
self.actor_network.update_target()
self.critic_network.update_target()
def noise_action(self, state):
action = self.actor_network.action(state)
exp = self.exploration_noise.noise()
t = action * exp
return exp
def action(self, state):
if np.random.rand() <= self.epsilon:
act = self.noise_action(state)
z = array(act)
else:
action = self.actor_network.action(state)
z = array(action)
self.mean.append(z[0])
g = np.tanh(z)
return g
def perceive(self, state, action, reward, next_state, done):
self.replay_buffer.add(state, action, reward, next_state, done)
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.train()
if self.epsilon > 0.1:
self.epsilon *= 0.99999
if done:
self.exploration_noise.reset()
class DDPG:
"""docstring for DDPG"""
def __init__(self, env):
self.name = 'DDPG' # name for uploading results
self.environment = env
# Randomly initialize actor network and critic network
# with both their target networks
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess,self.state_dim,self.action_dim)
self.critic_network = CriticNetwork(self.sess,self.state_dim,self.action_dim)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(self.action_dim)
def train(self):
#print "train step",self.time_step
# Sample a random minibatch of N transitions from replay buffer
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch,[BATCH_SIZE,self.action_dim])
# Calculate y_batch
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else :
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch,[BATCH_SIZE,1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch,state_batch,action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(state_batch,action_batch_for_gradients)
self.actor_network.train(q_gradient_batch,state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def noise_action(self,state):
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.action(state)
return action+self.exploration_noise.noise()
def action(self,state):
action = self.actor_network.action(state)
return action
def perceive(self,state,action,reward,next_state,done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
self.replay_buffer.add(state,action,reward,next_state,done)
# Store transitions to replay start size then start training
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.train()
#if self.time_step % 10000 == 0:
#self.actor_network.save_network(self.time_step)
#self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
class DDPG:
"""docstring for DDPG"""
def __init__(self, state_dim, action_dim):
"""name for uploading resuults"""
self.name = 'DDPG'
self.time_step = 0
# self.atten_rate = 1
"""Randomly initialize actor network and critic network"""
"""and both their target networks"""
self.state_dim = state_dim
self.action_dim = action_dim
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess, self.state_dim,
self.action_dim)
self.critic_network = CriticNetwork(self.sess, self.state_dim,
self.action_dim)
"""initialize replay buffer"""
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
"""Initialize a random process the Ornstein-Uhlenbeck process for action exploration"""
self.exploration_noise = OUNoise(self.action_dim)
"""Initialize a Treading"""
self.threading = threading.Thread(target=self.train,
name='LoopThread--DDPG')
def train(self):
# if self.time_step ==0:
# print("Begins Training!!!")
#print("Training Begins")
self.time_step += 1
"""Sample a random minibatch of N transitions from replay buffer"""
"""take out BATCH_SIZE sets of data"""
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
"""resize the action_batch shape to [BATCH_SIZE, self.action_dim]"""
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
"""Calculate y_batch(reward)"""
next_action_batch = self.actor_network.target_action(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch,
next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
"""Update critic by minimizing the loss L (training)"""
self.critic_network.train(y_batch, state_batch, action_batch)
"""Update the actor policy using the sampled gradient:"""
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients)
self.actor_network.train(q_gradient_batch, state_batch)
"""Update the target networks"""
self.actor_network.update_target()
self.critic_network.update_target()
#print("Training Finished")
def noise_action(self, state):
"""Select action a_t according to the current policy and exploration noise"""
action = self.actor_network.action(state)
exp_noise = self.exploration_noise.noise()
action += exp_noise
# action[0] = np.clip(action[0], 0, 1)
# action[1] = np.clip(action[1], -1, 1)
return action
def action(self, state):
action = self.actor_network.action(state)
# action[0] = np.clip(action[0], 0, 1)
# action[1] = np.clip(action[1], -1, 1)
return action
def perceive(self, state, action, reward, next_state, done):
"""Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer"""
self.replay_buffer.add(state, action, reward, next_state, done)
"""Store transitions to replay start size then start training"""
# if self.replay_buffer.count() % 1000 == 0:
# print("The buffer count is ", self.replay_buffer.count())
if self.replay_buffer.count() > REPLAY_START_SIZE:
self.train()
# self.atten_rate *= 0.99995
if not self.threading.is_alive():
self.threading = threading.Thread(target=self.train,
name='LoopThread--DDPG')
self.threading.start()
"""SAVE NETWORK"""
if self.time_step % 100 == 0:
print("Training_time_step:", self.time_step)
if self.time_step % 1000 == 0:
print("!!!!!!!save model success!!!!!!!!")
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
"""Re-iniitialize the random process when an episode ends"""
if done:
self.exploration_noise.reset()
class DDPG:
"""docstring for DDPG"""
def __init__(self, a_dim, s_dim):
self.name = 'DDPG' # name for uploading results
# self.environment = env
# Randomly initialize actor network and critic network
# with both their target networks
self.state_dim = s_dim
self.action_dim = a_dim
self.time_step=0
self.max_bw = 0.0
self.max_cwnd = 0.0
self.min_rtt = 9999999.0
self.sess = tf.InteractiveSession()
self.actor_network = ActorNetwork(self.sess, self.state_dim, self.action_dim)
self.critic_network = CriticNetwork(self.sess, self.state_dim, self.action_dim)
# initialize replay buffer
self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(self.action_dim)
def learn(self):
# print "train step",self.time_step
# Sample a random minibatch of N transitions from replay buffer
minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
state_batch = np.asarray([data[0] for data in minibatch])
action_batch = np.asarray([data[1] for data in minibatch])
reward_batch = np.asarray([data[2] for data in minibatch])
next_state_batch = np.asarray([data[3] for data in minibatch])
done_batch = np.asarray([data[4] for data in minibatch])
# for action_dim = 1
action_batch = np.resize(action_batch, [BATCH_SIZE, self.action_dim])
# Calculate y_batch
next_action_batch = self.actor_network.target_actions(next_state_batch)
q_value_batch = self.critic_network.target_q(next_state_batch, next_action_batch)
y_batch = []
for i in range(len(minibatch)):
if done_batch[i]:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
y_batch = np.resize(y_batch, [BATCH_SIZE, 1])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, state_batch, action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.actions(state_batch)
q_gradient_batch = self.critic_network.gradients(state_batch, action_batch_for_gradients)
self.actor_network.train(q_gradient_batch, state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def noise_action(self, state):
self.time_step += 1
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.action(state)
noise = self.exploration_noise.noise()
# print("noise:" + str(noise))
return action + noise
def choose_action(self, state):
self.time_step += 1
# print("_______________________choose_action_____________________")
action = self.actor_network.action(state)
return action
def store_transition(self, s, a, r, s_,done,episode_count):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
# print("*********************************ADD****************************")
self.replay_buffer.add(s, a, r, s_, done)
# Store transitions to replay start size then start training
if self.replay_buffer.count() > REPLAY_START_SIZE:
if((episode_count+1)%100!= 0):
self.learn()
# print("learn!")
else:
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
def extract_observation(self,dataRecorder,subflow_index,state_before):
# print("extracting...")
value_dic = dataRecorder.get_latest_data()
state_after=state_before.reshape(10,5)
# observation = np.zeros((4))
observation = np.zeros((5))
t_cWnd=[0,0]
t_thr=[0,0]
t_rtt=[0,0]
t_loss_rate=[0,0]
t_unAck=[0,0]
s0=[0,0,0,0,0]
state=np.zeros(1)
for i in range(value_dic["nbOfSubflows"]):
name = "cWnd" + str(i)
t_cWnd[i] = value_dic[name]
name = "rtt"+str(i)
t_rtt[i] = value_dic[name]
name = "unAck" + str(i)
t_unAck[i]=value_dic[name]
name = "loss_rate" + str(i)
t_loss_rate[i]=value_dic[name]
name = "throughput" + str(i)
t_thr[i]=value_dic[name]
thr=t_thr[subflow_index]
s0[0]=t_thr[subflow_index]
rtt=t_rtt[subflow_index]
s0[1]=t_rtt[subflow_index]
cwnd=t_cWnd[subflow_index]
s0[2]=t_cWnd[subflow_index]
loss_rate=t_loss_rate[subflow_index]
s0[3]=t_loss_rate[subflow_index]
unAck=t_unAck[subflow_index]
s0[4]=t_unAck[subflow_index]
s0=np.array(s0)
min_=s0-s0
thr_n=s0[0]
thr_n_min=s0[0]-min_[0]
rtt_min=s0[1]-min_[1]
cwnd_n_min=s0[2]-min_[2]
loss_rate_n_min=s0[3]-min_[3]
unAck_n_min=s0[4]-min_[4]
# loss_rate_n_min=s0[7]-min_[7]
if self.max_bw<thr_n_min:
self.max_bw=thr_n_min
if self.max_cwnd<cwnd_n_min:
self.max_cwnd=cwnd_n_min
if self.max_cwnd<cwnd_n_min:
self.max_cwnd=cwnd_n_min
if self.min_rtt>rtt_min:
self.min_rtt=rtt_min
reward = thr_n_min-5*(rtt_min-self.min_rtt)-10*loss_rate_n_min
print("reward:"+str(reward)+" thr_n_min:"+str(thr_n_min)+ " rtt_min:"+str(rtt_min)+" self.min_rtt :"+str(self.min_rtt)+" delta_rtt"+str(rtt_min-self.min_rtt))
# print("unAck:"+str(unAck_n_min))
if self.max_bw!=0:
state[0]=thr_n_min/self.max_bw
# tmp=pacing_rate_n_min/self.max_bw
state=np.append(state,[5*loss_rate_n_min])
state=np.append(state,[unAck_n_min])
else:
state[0]=0
state=np.append(state,[0])
state=np.append(state,[0])
state=np.append(state,[1400/cwnd])
state=np.append(state,[self.min_rtt/rtt_min])
state_after=np.delete(state_after,[0],axis = 0)
state_after=np.append(state_after,state)
return state_after,reward,thr_n_min,rtt_min