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ACNet.py
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ACNet.py
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import tensorflow as tf
import tensorflow.contrib.layers as layers
import numpy as np
#parameters for training
GRAD_CLIP = 1000.0
KEEP_PROB1 = 1 # was 0.5
KEEP_PROB2 = 1 # was 0.7
RNN_SIZE = 512
GOAL_REPR_SIZE = 12
#Used to initialize weights for policy and value output layers (Do we need to use that? Maybe not now)
def normalized_columns_initializer(std=1.0):
def _initializer(shape, dtype=None, partition_info=None):
out = np.random.randn(*shape).astype(np.float32)
out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
return tf.constant(out)
return _initializer
class ACNet:
def __init__(self, scope, a_size, trainer,TRAINING,GRID_SIZE,GLOBAL_NET_SCOPE):
with tf.variable_scope(str(scope)+'/qvalues'):
#The input size may require more work to fit the interface.
self.inputs = tf.placeholder(shape=[None,4,GRID_SIZE,GRID_SIZE], dtype=tf.float32)
self.goal_pos=tf.placeholder(shape=[None,3],dtype=tf.float32)
self.myinput = tf.transpose(self.inputs, perm=[0,2,3,1])
self.policy, self.value, self.state_out, self.state_in, self.state_init, self.blocking, self.on_goal,self.valids = self._build_net(self.myinput,self.goal_pos,RNN_SIZE,TRAINING,a_size)
if TRAINING:
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, a_size, dtype=tf.float32)
self.train_valid = tf.placeholder(shape=[None,a_size], dtype=tf.float32)
self.target_v = tf.placeholder(tf.float32, [None], 'Vtarget')
self.advantages = tf.placeholder(shape=[None], dtype=tf.float32)
self.target_blockings = tf.placeholder(tf.float32, [None])
self.target_on_goals = tf.placeholder(tf.float32, [None])
self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1])
self.train_value = tf.placeholder(tf.float32, [None])
self.optimal_actions = tf.placeholder(tf.int32,[None])
self.optimal_actions_onehot = tf.one_hot(self.optimal_actions, a_size, dtype=tf.float32)
# Loss Functions
self.value_loss = tf.reduce_sum(self.train_value*tf.square(self.target_v - tf.reshape(self.value, shape=[-1])))
self.entropy = - tf.reduce_sum(self.policy * tf.log(tf.clip_by_value(self.policy,1e-10,1.0)))
self.policy_loss = - tf.reduce_sum(tf.log(tf.clip_by_value(self.responsible_outputs,1e-15,1.0)) * self.advantages)
self.valid_loss = - tf.reduce_sum(tf.log(tf.clip_by_value(self.valids,1e-10,1.0)) *\
self.train_valid+tf.log(tf.clip_by_value(1-self.valids,1e-10,1.0)) * (1-self.train_valid))
self.blocking_loss = - tf.reduce_sum(self.target_blockings*tf.log(tf.clip_by_value(self.blocking,1e-10,1.0))\
+(1-self.target_blockings)*tf.log(tf.clip_by_value(1-self.blocking,1e-10,1.0)))
self.on_goal_loss = - tf.reduce_sum(self.target_on_goals*tf.log(tf.clip_by_value(self.on_goal,1e-10,1.0))\
+(1-self.target_on_goals)*tf.log(tf.clip_by_value(1-self.on_goal,1e-10,1.0)))
self.loss = 0.5 * self.value_loss + self.policy_loss + 0.5*self.valid_loss \
- self.entropy * 0.01 +.5*self.blocking_loss
self.imitation_loss = tf.reduce_mean(tf.contrib.keras.backend.categorical_crossentropy(self.optimal_actions_onehot,self.policy))
# Get gradients from local network using local losses and
# normalize the gradients using clipping
local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope+'/qvalues')
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, GRAD_CLIP)
# Apply local gradients to global network
global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, GLOBAL_NET_SCOPE+'/qvalues')
self.apply_grads = trainer.apply_gradients(zip(grads, global_vars))
#now the gradients for imitation loss
self.i_gradients = tf.gradients(self.imitation_loss, local_vars)
self.i_var_norms = tf.global_norm(local_vars)
i_grads, self.i_grad_norms = tf.clip_by_global_norm(self.i_gradients, GRAD_CLIP)
# Apply local gradients to global network
self.apply_imitation_grads = trainer.apply_gradients(zip(i_grads, global_vars))
print("Hello World... From "+str(scope)) # :)
def _build_net(self,inputs,goal_pos,RNN_SIZE,TRAINING,a_size):
w_init = layers.variance_scaling_initializer()
conv1 = layers.conv2d(inputs=inputs, padding="SAME", num_outputs=RNN_SIZE//4, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
conv1a = layers.conv2d(inputs=conv1, padding="SAME", num_outputs=RNN_SIZE//4, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
conv1b = layers.conv2d(inputs=conv1a, padding="SAME", num_outputs=RNN_SIZE//4, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
pool1 = layers.max_pool2d(inputs=conv1b,kernel_size=[2,2])
conv2 = layers.conv2d(inputs=pool1, padding="SAME", num_outputs=RNN_SIZE//2, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
conv2a = layers.conv2d(inputs=conv2, padding="SAME", num_outputs=RNN_SIZE//2, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
conv2b = layers.conv2d(inputs=conv2a, padding="SAME", num_outputs=RNN_SIZE//2, kernel_size=[3, 3], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=tf.nn.relu)
pool2 = layers.max_pool2d(inputs=conv2b,kernel_size=[2,2])
conv3 = layers.conv2d(inputs=pool2, padding="VALID", num_outputs=RNN_SIZE-GOAL_REPR_SIZE, kernel_size=[2, 2], stride=1, data_format="NHWC", weights_initializer=w_init,activation_fn=None)
flat = tf.nn.relu(layers.flatten(conv3))
goal_layer= layers.fully_connected(inputs=goal_pos, num_outputs=GOAL_REPR_SIZE)
hidden_input=tf.concat([flat,goal_layer],1)
h1 = layers.fully_connected(inputs=hidden_input, num_outputs=RNN_SIZE)
d1 = layers.dropout(h1, keep_prob=KEEP_PROB1, is_training=TRAINING)
h2 = layers.fully_connected(inputs=d1, num_outputs=RNN_SIZE, activation_fn=None)
d2 = layers.dropout(h2, keep_prob=KEEP_PROB2, is_training=TRAINING)
self.h3 = tf.nn.relu(d2+hidden_input)
#Recurrent network for temporal dependencies
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(RNN_SIZE,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)
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])
state_in = (c_in, h_in)
rnn_in = tf.expand_dims(self.h3, [0])
step_size = tf.shape(inputs)[:1]
state_in = tf.nn.rnn_cell.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
state_out = (lstm_c[:1, :], lstm_h[:1, :])
self.rnn_out = tf.reshape(lstm_outputs, [-1, RNN_SIZE])
policy_layer = layers.fully_connected(inputs=self.rnn_out, num_outputs=a_size,weights_initializer=normalized_columns_initializer(1./float(a_size)), biases_initializer=None, activation_fn=None)
policy = tf.nn.softmax(policy_layer)
policy_sig = tf.sigmoid(policy_layer)
value = layers.fully_connected(inputs=self.rnn_out, num_outputs=1, weights_initializer=normalized_columns_initializer(1.0), biases_initializer=None, activation_fn=None)
blocking = layers.fully_connected(inputs=self.rnn_out, num_outputs=1, weights_initializer=normalized_columns_initializer(1.0), biases_initializer=None, activation_fn=tf.sigmoid)
on_goal = layers.fully_connected(inputs=self.rnn_out, num_outputs=1, weights_initializer=normalized_columns_initializer(1.0), biases_initializer=None, activation_fn=tf.sigmoid)
return policy, value, state_out ,state_in, state_init, blocking, on_goal,policy_sig