def __init__(self, s_size, a_size, scope, trainer):
with tf.variable_scope(scope):
# Input and visual encoding layers
self.inputs = tf.placeholder(shape=[None, s_size],
dtype=tf.float32)
self.imageIn = tf.reshape(self.inputs, shape=[-1, 64, 64, 1])
self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
inputs=self.imageIn,
num_outputs=16,
kernel_size=[8, 8],
stride=[4, 4],
padding='VALID')
self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,
inputs=self.conv1,
num_outputs=32,
kernel_size=[4, 4],
stride=[2, 2],
padding='VALID')
hidden = slim.fully_connected(slim.flatten(self.conv2),
256,
activation_fn=tf.nn.elu)
# Recurrent network for temporal dependencies
lstm_cell = tf.contrib.rnn.BasicLSTMCell(256, state_is_tuple=True)
c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
self.state_in = (c_in, h_in)
rnn_in = tf.expand_dims(hidden, [0])
step_size = tf.shape(self.imageIn)[:1]
state_in = tf.contrib.rnn.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm_cell,
rnn_in,
initial_state=state_in,
sequence_length=step_size,
time_major=False)
lstm_c, lstm_h = lstm_state
self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
rnn_out = tf.reshape(lstm_outputs, [-1, 256])
# Output layers for policy and value estimations
self.policy = slim.fully_connected(
rnn_out,
a_size,
#activation_fn=tf.nn.softmax,
activation_fn=tf.nn.tanh, #TODO Changed.
weights_initializer=normalized_columns_initializer(0.1),
biases_initializer=None)
self.value = slim.fully_connected(
rnn_out,
1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=None)
# Only the worker network need ops for loss functions and gradient updating.
if scope != 'global':
self.actions = tf.placeholder(shape=[None], dtype=tf.float32)
# self.actions_onehot = tf.one_hot(self.actions, a_size, dtype=tf.float32)
#self.actions_steering = tf.placeholder(shape=[None], dtype=tf.float32) # TODO Changed.
self.target_v = tf.placeholder(shape=[None], dtype=tf.float32)
self.advantages = tf.placeholder(shape=[None],
dtype=tf.float32)
#steering=tf.Tensor.eval(self.actions)
#self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1])
#self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_steering, [1]) # TODO Changed.
# Loss functions
self.value_loss = 0.5 * tf.reduce_sum(
tf.square(self.target_v - tf.reshape(self.value, [-1])))
self.entropy = -tf.reduce_sum(
self.policy * tf.log(self.policy))
#self.policy_loss = -tf.reduce_sum(tf.log(self.responsible_outputs) * self.advantages)
self.policy_loss = -tf.reduce_sum(
tf.log(self.actions) * self.advantages)
self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01
# Get gradients from local network using local losses
local_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope)
self.gradients = tf.gradients(self.loss, local_vars)
self.var_norms = tf.global_norm(local_vars)
grads, self.grad_norms = tf.clip_by_global_norm(
self.gradients, 40.0)
# Apply local gradients to global network
global_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'global')
self.apply_grads = trainer.apply_gradients(
zip(grads, global_vars))
def __init__(self,
s_size,
s_size_central,
number_of_agents,
a_size,
comm_size_input,
comm_size_output,
scope,
trainer,
critic_action=False,
critic_comm=False):
with tf.variable_scope(scope):
print("Scope", scope)
if critic_action and critic_comm:
central_input_size = [
s_size_central[0] + (number_of_agents - 1) * a_size +
comm_size_input
]
elif critic_comm:
central_input_size = [s_size_central[0] + comm_size_input]
elif critic_action:
central_input_size = [
s_size_central[0] + (number_of_agents - 1) * a_size
]
else:
central_input_size = s_size_central
self.inputs = tf.placeholder(shape=[
None,
] + s_size,
dtype=tf.float32)
self.inputs_central = tf.placeholder(shape=[
None,
] + central_input_size,
dtype=tf.float32)
self.inputs_comm = tf.placeholder(shape=[None, comm_size_input],
dtype=tf.float32)
flattened_inputs = tf.contrib.layers.flatten(self.inputs)
self.flattened_inputs_with_comm = tf.concat(
[flattened_inputs, self.inputs_comm], 1)
hidden_comm = slim.fully_connected(
flattened_inputs,
40,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
#hidden2_comm = slim.fully_connected(hidden_comm, 20,
# weights_initializer=tf.contrib.layers.xavier_initializer(),
# biases_initializer=tf.contrib.layers.xavier_initializer(),
# activation_fn=tf.nn.relu)
hidden = slim.fully_connected(
self.flattened_inputs_with_comm,
80,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
hidden2 = slim.fully_connected(
hidden,
40,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
hidden_central = slim.fully_connected(
self.inputs_central,
80,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
hidden2_central = slim.fully_connected(
hidden_central,
40,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
self.value = slim.fully_connected(
hidden2_central,
1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=normalized_columns_initializer(1.0))
self.policy = slim.fully_connected(
hidden2,
a_size,
activation_fn=tf.nn.softmax,
weights_initializer=normalized_columns_initializer(0.01),
biases_initializer=normalized_columns_initializer(0.01))
if comm_size_output != 0:
self.message = slim.fully_connected(
hidden_comm,
comm_size_output,
activation_fn=tf.nn.tanh,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.contrib.layers.xavier_initializer())
else:
self.message = slim.fully_connected(hidden_comm,
comm_size_output)
# Only the worker network need ops for loss functions and gradient updating.
if scope != 'global':
self.actions = tf.placeholder(
shape=[None], dtype=tf.int32) # Index of actions taken
self.actions_onehot = tf.one_hot(
self.actions, a_size,
dtype=tf.float32) # 1-hot tensor of actions taken
self.target_v = tf.placeholder(
shape=[None], dtype=tf.float32) # Target Value
self.advantages = tf.placeholder(
shape=[None],
dtype=tf.float32) # temporary difference (R-V)
self.log_policy = tf.log(
tf.clip_by_value(self.policy, 1e-20, 1.0)
) # avoid NaN with clipping when value in policy becomes zero
self.responsible_outputs = tf.reduce_sum(
self.log_policy * self.actions_onehot,
[1]) # Get policy*actions influence
self.r_minus_v = self.target_v - tf.reshape(
self.value,
[-1]) # difference between target value and actual value
# Loss functions
self.value_loss = 0.5 * tf.reduce_sum(tf.square(
self.r_minus_v)) # same as tf.nn.l2_loss(r_minus_v)
self.entropy = -tf.reduce_sum(
self.policy * self.log_policy) # policy entropy
self.policy_loss = -tf.reduce_sum(
self.responsible_outputs * self.advantages) # policy loss
# loss of message
self.target_message = tf.placeholder('float32',
[None, comm_size_output],
name='target_message')
self.loss_m = tf.reduce_mean(tf.square(self.target_message -
self.message),
name='loss_m')
# Learning rate for Critic is half of Actor's, so value_loss/2 + policy loss
self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01
# Get gradients from local network using local losses
self.local_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope)
self.gradients = tf.gradients(self.loss, self.local_vars)
self.var_norms = tf.global_norm(self.local_vars)
grads, self.grad_norms = tf.clip_by_global_norm(
self.gradients, 40.0)
# gradients of "loss" wrt message
self.gradients_q_message = tf.gradients(
self.policy_loss, self.inputs_comm)
# gradients of "target message" wrt weights
self.gradients_m_weights = tf.gradients(
self.loss_m, self.local_vars)
grads_m, self.grad_norms_m = tf.clip_by_global_norm(
self.gradients_m_weights, 40.0)
# Apply local gradients to global network
self.global_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'global')
self.apply_grads = trainer.apply_gradients(
zip(grads, self.global_vars))
self.apply_grads_m = trainer.apply_gradients(
zip(grads_m, self.global_vars))
def __init__(self,
s_size,
a_size,
scope,
trainer,
use_conv_layers=False,
use_lstm=False):
with tf.variable_scope(scope):
print("Scope", scope)
# Input and visual encoding layers
if use_conv_layers:
self.inputs = tf.placeholder(shape=[None, s_size],
dtype=tf.float32)
self.imageIn = tf.reshape(self.inputs,
shape=[-1, 1280, 800, 1])
self.conv = slim.conv2d(
activation_fn=tf.nn.elu,
weights_initializer=tf.contrib.layers.xavier_initializer(),
# normalized_columns_initializer(0.01),
inputs=self.imageIn,
num_outputs=8,
kernel_size=[3, 3],
stride=[1, 1],
padding='VALID')
self.conv2 = slim.conv2d(
activation_fn=tf.nn.elu,
weights_initializer=tf.contrib.layers.xavier_initializer(),
# normalized_columns_initializer(0.01),
inputs=self.imageIn,
num_outputs=4,
kernel_size=[1, 1],
stride=[1, 1],
padding='VALID')
hidden = slim.fully_connected(
slim.flatten(self.conv2),
150,
weights_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.elu)
hidden2 = slim.fully_connected(
hidden,
150,
weights_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.elu)
else:
self.inputs = tf.placeholder(shape=[None, s_size],
dtype=tf.float32)
# hidden = slim.fully_connected(self.inputs, 150,
# weights_initializer=tf.contrib.layers.xavier_initializer(),
# activation_fn=tf.nn.elu)
# hidden2 = slim.fully_connected(hidden, 150, weights_initializer=tf.contrib.layers.xavier_initializer(),
# activation_fn=tf.nn.elu)
hidden2 = slim.fully_connected(
self.inputs,
150,
weights_initializer=tf.contrib.layers.xavier_initializer(),
activation_fn=tf.nn.relu)
if use_lstm:
# Recurrent network for temporal dependencies
lstm_cell = tf.contrib.rnn.BasicLSTMCell(256,
state_is_tuple=True)
c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
self.state_in = (c_in, h_in)
rnn_in = tf.expand_dims(
hidden2, [0]) # converts hidden layer [256] to [1, 256]
if use_conv_layers:
step_size = tf.shape(self.imageIn)[:1]
else:
step_size = tf.shape(self.inputs)[:1]
state_in = tf.contrib.rnn.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm_cell,
rnn_in,
initial_state=state_in,
sequence_length=step_size,
time_major=False)
lstm_c, lstm_h = lstm_state
self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
rnn_out = tf.reshape(lstm_outputs, [-1, 256])
# Output layers for policy and value estimations
self.policy = slim.fully_connected(
rnn_out,
a_size,
activation_fn=tf.nn.softmax,
weights_initializer=normalized_columns_initializer(0.01),
biases_initializer=None)
self.value = slim.fully_connected(
rnn_out,
1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=None)
else:
self.state_init = None
self.policy = slim.fully_connected(
hidden2,
a_size,
activation_fn=tf.nn.softmax,
weights_initializer=normalized_columns_initializer(0.01),
biases_initializer=None)
self.value = slim.fully_connected(
hidden2,
1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=None)
# Only the worker network need ops for loss functions and gradient updating.
if scope != 'global_square' and scope != 'global_circle':
self.actions = tf.placeholder(
shape=[None], dtype=tf.int32) # Index of actions taken
self.actions_onehot = tf.one_hot(
self.actions, a_size,
dtype=tf.float32) # 1-hot tensor of actions taken
self.target_v = tf.placeholder(
shape=[None], dtype=tf.float32) # Target Value
self.advantages = tf.placeholder(
shape=[None],
dtype=tf.float32) # temporary difference (R-V)
self.log_policy = tf.log(
tf.clip_by_value(self.policy, 1e-20, 1.0)
) # avoid NaN with clipping when value in policy becomes zero
self.responsible_outputs = tf.reduce_sum(
self.log_policy * self.actions_onehot,
[1]) # Get policy*actions influence
self.r_minus_v = self.target_v - tf.reshape(
self.value,
[-1]) # difference between target value and actual value
# Loss functions
self.value_loss = 0.5 * tf.reduce_sum(tf.square(
self.r_minus_v)) # same as tf.nn.l2_loss(r_minus_v)
self.entropy = -tf.reduce_sum(
self.policy * self.log_policy) # policy entropy
self.policy_loss = -tf.reduce_sum(
self.responsible_outputs * self.advantages) # policy loss
# Learning rate for Critic is half of Actor's, so value_loss/2 + policy loss
self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01
# Get gradients from local network using local losses
self.local_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope)
self.gradients = tf.gradients(self.loss, self.local_vars)
self.var_norms = tf.global_norm(self.local_vars)
grads, self.grad_norms = tf.clip_by_global_norm(
self.gradients, 40.0)
# Apply local gradients to global network
if "square" in scope:
global_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'global_square')
elif "circle" in scope:
global_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'global_circle')
else:
print("Error on scope build", scope)
exit()
self.apply_grads = trainer.apply_gradients(
zip(grads, global_vars))
