def sac_cnn_lstm(scaled_images, **kwargs):
activ = tf.nn.relu
conv1 = activ(
conv(scaled_images,
'c1',
n_filters=32,
filter_size=5,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
conv2 = activ(
conv(conv1,
'c2',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
conv3 = activ(
conv(conv2,
'c3',
n_filters=64,
filter_size=3,
stride=2,
init_scale=np.sqrt(2),
**kwargs))
conv3 = conv_to_fc(conv3)
# try w/o LSTM first
return activ(linear(conv3, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def modified_cnn(scaled_images, **kwargs):
activ = tf.nn.relu
layer_1 = activ(conv(scaled_images, 'c1', n_filters=64, filter_size=2, stride=1, **kwargs))
layer_2 = activ(conv(layer_1, 'c2', n_filters=128, filter_size=2, stride=1, **kwargs))
layer_3 = activ(conv(layer_2, 'c3', n_filters=256, filter_size=2, stride=1, **kwargs))
layer_3 = conv_to_fc(layer_3)
return activ(linear(layer_3, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def Cnn1(image, **kwargs):
activ = tf.nn.relu
layer_1 = activ(
conv(image,
'c1',
n_filters=32,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_2 = activ(
conv(layer_1,
'c2',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = activ(
conv(layer_2,
'c3',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = conv_to_fc(layer_3)
return activ(linear(layer_3, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **kwargs):
super(NatureCNN, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=True)
with tf.variable_scope("model", reuse=reuse):
activ = tf.nn.relu
input = self.processed_obs
layer_1 = activ(conv(input, 'c1', n_filters=32, filter_size=8, stride=4, init_scale=np.sqrt(2), **kwargs))
layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=4, stride=2, init_scale=np.sqrt(2), **kwargs))
layer_3 = activ(conv(layer_2, 'c3', n_filters=64, filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs))
layer_3 = conv_to_fc(layer_3)
extracted_features = activ(linear(layer_3, 'fc1', n_hidden=256, init_scale=np.sqrt(2)))
value_fn = tf.layers.dense(extracted_features, 1, name='vf')
self.proba_distribution, self.policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(extracted_features, extracted_features, init_scale=0.01)
self.value_fn = value_fn
self.initial_state = None
self._setup_init()
total = 0
for v in tf.trainable_variables():
dims = v.get_shape().as_list()
num = int(np.prod(dims))
total += num
print(' %s \t\t Num: %d \t\t Shape %s ' % (v.name, num, dims))
print('\nTotal number of params: %d' % total)
def ppo_cnn(scaled_images, **kwargs):
activ = tf.nn.elu
conv1 = activ(
conv(scaled_images,
'c1',
n_filters=32,
filter_size=5,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
conv2 = activ(
conv(conv1,
'c2',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
conv3 = activ(
conv(conv2,
'c3',
n_filters=64,
filter_size=3,
stride=2,
init_scale=np.sqrt(2),
**kwargs))
conv3 = conv_to_fc(conv3)
return activ(linear(conv3, 'fc1', n_hidden=512, init_scale=0.01))
def proba_distribution_from_latent_infer(self,
infer_latent_vector,
init_scale=1.0,
init_bias=0.0,
std_normal=False,
prior_std=0):
if std_normal:
pdparam = tf.concat([
tf.zeros([1, self.size]), prior_std * tf.ones([1, self.size])
],
axis=1)
mean = tf.zeros([1, self.size])
return self.proba_distribution_from_flat(pdparam), mean
else:
mean = linear(infer_latent_vector,
'infer',
self.size,
init_scale=init_scale,
init_bias=init_bias)
logstd = tf.get_variable(name='infer/logstd',
shape=[1, self.size],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
return self.proba_distribution_from_flat(pdparam), mean
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=False,
net_arch=None,
act_fun=tf.tanh,
feature_extraction="cnn",
**kwargs):
super(RelationalPolicy,
self).__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=reuse,
scale=(feature_extraction == "cnn"))
self._kwargs_check(feature_extraction, kwargs)
with tf.variable_scope("model", reuse=reuse):
print('self.processed_obs', self.processed_obs)
relation_block_output = self.relation_block(self.processed_obs)
pi_latent = vf_latent = tf.layers.flatten(relation_block_output)
# original code
self._value_fn = linear(vf_latent, 'vf', 1)
self._proba_distribution, self._policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(pi_latent, vf_latent, init_scale=0.01)
self._setup_init()
def tic_tac_toe_cnn(scaled_images, **kwargs):
"""
Custom CNN for Tic Tac Toe env.
:param scaled_images: (TensorFlow Tensor) Image input placeholder
:return: (TensorFlow Tensor) The CNN output layer
"""
activ = tf.nn.relu
layer = scaled_images
# print(kwargs)
net_arch = kwargs['cnn_arch']
filter_size = kwargs['filter_size']
pad = kwargs['pad']
for i, f in enumerate(net_arch[:-1], start=1):
# print('c' + str(i), f)
layer = activ(conv(layer, 'c' + str(i), n_filters=f, filter_size=filter_size,
stride=1, pad=pad, data_format='NCHW'))
layer = conv_to_fc(layer)
# print('fc1', net_arch[-1])
# print()
return activ(linear(layer, 'fc1', n_hidden=net_arch[-1]))
def cnn_3d(scaled_voxels, n_hidden, filters, filter_sizes, strides, **kwargs):
"""
CNN in 3D.
:param scaled_voxels: (TensorFlow Tensor) Voxel input placeholder
:param n_hidden: (int) Number of nodes in the last linear layer
:param filters: (array) Filter numbers for the convolutional layers of the CNN
:param filter_sizes: (array) Filter sizes for the convolutional layers of the CNN
:param strides: (array) Strides for the convolutional layers of the CNN
:param kwargs: (dict) Extra keywords parameters for the convolutional layers of the CNN
:return: (TensorFlow Tensor) The CNN output layer
"""
activ = tf.tanh
layers = []
for i, (n_filter, filter_size,
stride) in enumerate(zip(filters, filter_sizes, strides)):
input_layer = scaled_voxels if i == 0 else layers[-1]
label = 'c%d' % (i + 1)
layer = activ(
conv3d(input_layer,
label,
n_filters=n_filter,
filter_size=filter_size,
stride=stride,
init_scale=np.sqrt(2),
**kwargs))
layers.append(layer)
print('layer_%d' % (i + 1), layer.shape)
layer = conv_to_fc(layers[-1])
return tf.tanh(
linear(layer, 'fc1', n_hidden=n_hidden, init_scale=np.sqrt(2)))
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, layers=None, net_arch=None,
act_fun=tf.tanh, cnn_extractor=nature_cnn, feature_extraction="cnn", **kwargs):
super(FeedForwardPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse,
scale=(feature_extraction == "cnn"))
self._kwargs_check(feature_extraction, kwargs)
if layers is not None:
warnings.warn("Usage of the `layers` parameter is deprecated! Use net_arch instead "
"(it has a different semantics though).", DeprecationWarning)
if net_arch is not None:
warnings.warn("The new `net_arch` parameter overrides the deprecated `layers` parameter!",
DeprecationWarning)
if net_arch is None:
if layers is None:
layers = [64, 64]
net_arch = [dict(vf=layers, pi=layers)]
with tf.variable_scope("model", reuse=reuse):
if feature_extraction == "cnn":
pi_latent = vf_latent = cnn_extractor(self.processed_obs, **kwargs)
else:
pi_latent, vf_latent = mlp_extractor(tf.layers.flatten(self.processed_obs), net_arch, act_fun)
self.value_fn = linear(vf_latent, 'vf', 1)
self.proba_distribution, self.policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(pi_latent, vf_latent, init_scale=0.01)
self.initial_state = None
self._setup_init()
def embedding(entities, n_heads, embedding_sizes, scope):
"""
:param entities: (TensorFlow Tensor) The input entities : [B,N,D]
:param scope: (str) The TensorFlow variable scope
:param n_heads: (float) The number of attention heads to use
:return: (TensorFlow Tensor) [B,n_heads,N,embedding_sizes[i]]
"""
with tf.variable_scope(scope):
N = entities.shape[1].value
channels = entities.shape[2].value
# total_size Denoted as F, n_heads Denoted as H
total_size = sum(embedding_sizes) * n_heads
# [B*N,D]
entities = tf.reshape(entities, [-1, channels])
# [B*N,F] F = sum(embedding_sizes) * n_heads
embedded_entities = linear(entities, "mlp", total_size)
# [B*N,F] --> [B,N,F] new
embedded_entities = tf.reshape(embedded_entities, [-1, N, total_size])
# [B*N,F]
qkv = layerNorm(embedded_entities, "ln")
# qkv = batchNorm(embedded_entities, "bn")
# qkv = instanceNorm(embedded_entities, "instacne_n")
# qkv = FRNorm(embedded_entities, 'FRNorm')
# # [B,N,F]
# qkv = tf.reshape(qkv, [-1, N, total_size])
# [B,N,n_heads,sum(embedding_sizes)]
qkv = tf.reshape(qkv, [-1, N, n_heads, sum(embedding_sizes)])
# [B,N,n_heads,sum(embedding_sizes)] -> [B,n_heads,N,sum(embedding_sizes)]
qkv = tf.transpose(qkv, [0, 2, 1, 3])
return tf.split(qkv, embedding_sizes, -1)
def minigrid_extractor_small(scaled_images, **kwargs):
"""
CNN for MiniGrid environments with variable grid sizes
"""
activ = tf.nn.relu
# first layer is just an embedding finder
layer_1 = conv(scaled_images,
'c1',
n_filters=32,
filter_size=1,
stride=1,
init_scale=np.sqrt(2),
**kwargs)
layer_2 = activ(
conv(layer_1,
'c2',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = activ(
conv(layer_2,
'c3',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_4 = conv_to_fc(layer_3)
print(layer_3)
return activ(linear(layer_4, 'fc1', n_hidden=128, init_scale=np.sqrt(2)))
def proba_distribution_from_latent(self,
pi_latent_vector,
vf_latent_vector,
init_scale=1.0,
init_bias=0.0):
pdparam = linear(pi_latent_vector,
'pi',
self.size,
init_scale=init_scale,
init_bias=init_bias)
q_values = linear(vf_latent_vector,
'q',
self.size,
init_scale=init_scale,
init_bias=init_bias)
return self.proba_distribution_from_flat(pdparam), pdparam, q_values
def nature_cnn(scaled_images, **kwargs):
"""
CNN from Nature paper.
:param scaled_images: (TensorFlow Tensor) Image input placeholder
:param kwargs: (dict) Extra keywords parameters for the convolutional layers of the CNN
:return: (TensorFlow Tensor) The CNN output layer
"""
activ = tf.nn.relu
layer_1 = activ(
conv(scaled_images,
'c1',
n_filters=32,
filter_size=8,
stride=4,
init_scale=np.sqrt(2),
**kwargs))
layer_2 = activ(
conv(layer_1,
'c2',
n_filters=64,
filter_size=4,
stride=2,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = activ(
conv(layer_2,
'c3',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = conv_to_fc(layer_3)
return activ(linear(layer_3, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=256, reuse=False, layers=None,
net_arch=None, layer_norm=False, feature_extraction="cnn",
**kwargs):
# state_shape = [n_lstm * 2] dim because of the cell and hidden states of the LSTM
super(RelationalLstmPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch,
state_shape=(2 * n_lstm, ), reuse=reuse,
scale=(feature_extraction == "cnn"))
self._kwargs_check(feature_extraction, kwargs)
with tf.variable_scope("model", reuse=reuse):
print('self.processed_obs', self.processed_obs)
relation_block_output = self.relation_block(self.processed_obs)
# original code
input_sequence = batch_to_seq(relation_block_output, self.n_env, n_steps)
print('input_sequence', input_sequence)
masks = batch_to_seq(self.dones_ph, self.n_env, n_steps)
rnn_output, self.snew = lstm(input_sequence, masks, self.states_ph, 'lstm1', n_hidden=n_lstm,
layer_norm=layer_norm)
rnn_output = seq_to_batch(rnn_output)
value_fn = linear(rnn_output, 'vf', 1)
self._proba_distribution, self._policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(rnn_output, rnn_output)
self._value_fn = value_fn
self._setup_init()
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=False,
**kwargs):
super(NatureCNN, self).__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=reuse,
scale=True)
with tf.variable_scope("model", reuse=reuse):
activ = tf.nn.relu
input = self.processed_obs
layer_1 = activ(
conv(input,
'c1',
n_filters=32,
filter_size=8,
stride=4,
init_scale=np.sqrt(2),
**kwargs))
layer_2 = activ(
conv(layer_1,
'c2',
n_filters=64,
filter_size=4,
stride=2,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = activ(
conv(layer_2,
'c3',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
layer_3 = conv_to_fc(layer_3)
extracted_features = activ(
linear(layer_3, 'fc1', n_hidden=256, init_scale=np.sqrt(2)))
value_fn = tf.layers.dense(extracted_features, 1, name='vf')
self.proba_distribution, self.policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(extracted_features, extracted_features, init_scale=0.01)
self.value_fn = value_fn
self.initial_state = None
self._setup_init()
def modified_cnn(unscaled_images, **kwargs): import tensorflow as tf scaled_images = tf.cast(unscaled_images, tf.float32) / 255. activ = tf.nn.relu layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=1, stride=1, init_scale=np.sqrt(2), **kwargs)) layer_2 = activ(conv(layer_1, 'c2', n_filters=32, filter_size=2, stride=2, init_scale=np.sqrt(2), **kwargs)) layer_2 = conv_to_fc(layer_2) return activ(linear(layer_2, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def modified_cnn(scaled_images, **kwargs): import tensorflow as tf activ = tf.nn.relu layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs)) layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs)) layer_3 = activ(conv(layer_2, 'c3', n_filters=64, filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs)) layer_3 = conv_to_fc(layer_3) return activ(linear(layer_3, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def modified_cnn(scaled_images, **kwargs):
activ = tf.nn.relu
# layer 1
conv1 = conv(scaled_images, "c1", n_filters=128, filter_size=(1, 2), stride=1, init_scale=np.sqrt(2), **kwargs)
conv2 = conv(scaled_images, "c2", n_filters=128, filter_size=(2, 1), stride=1, init_scale=np.sqrt(2), **kwargs)
relu1 = activ(conv1)
relu2 = activ(conv2)
# layer 2
conv11 = conv(relu1, "c3", n_filters=128, filter_size=(1, 2), stride=1, init_scale=np.sqrt(2), **kwargs)
conv12 = conv(relu1, "c4", n_filters=128, filter_size=(2, 1), stride=1, init_scale=np.sqrt(2), **kwargs)
conv21 = conv(relu2, "c3", n_filters=128, filter_size=(1, 2), stride=1, init_scale=np.sqrt(2), **kwargs)
conv22 = conv(relu2, "c4", n_filters=128, filter_size=(2, 1), stride=1, init_scale=np.sqrt(2), **kwargs)
# layer2 relu activation
relu11 = tf.nn.relu(conv11)
relu12 = tf.nn.relu(conv12)
relu21 = tf.nn.relu(conv21)
relu22 = tf.nn.relu(conv22)
# get shapes of all activations
shape1 = relu1.get_shape().as_list()
shape2 = relu2.get_shape().as_list()
shape11 = relu11.get_shape().as_list()
shape12 = relu12.get_shape().as_list()
shape21 = relu21.get_shape().as_list()
shape22 = relu22.get_shape().as_list()
# expansion
hidden1 = tf.reshape(relu1, [-1, shape1[1] * shape1[2] * shape1[3]])
hidden2 = tf.reshape(relu2, [-1, shape2[1] * shape2[2] * shape2[3]])
hidden11 = tf.reshape(relu11, [-1, shape11[1] * shape11[2] * shape11[3]])
hidden12 = tf.reshape(relu12, [-1, shape12[1] * shape12[2] * shape12[3]])
hidden21 = tf.reshape(relu21, [-1, shape21[1] * shape21[2] * shape21[3]])
hidden22 = tf.reshape(relu22, [-1, shape22[1] * shape22[2] * shape22[3]])
# concatenation
hidden = tf.concat([hidden1, hidden2, hidden11, hidden12, hidden21, hidden22], axis=1)
# linear layer 1
linear_1 = activ(linear(hidden, scope="fc1", n_hidden=512, init_scale=np.sqrt(2)))
# linear layer 2
linear_2 = activ(linear(linear_1, scope="fc2", n_hidden=128, init_scale=np.sqrt(2)))
return linear_2
def proba_distribution_from_latent(self,
pi_latent_vector,
vf_latent_vector,
init_scale=1.0,
init_bias=0.0,
mult_tensors=None,
policy=None):
if mult_tensors is not None:
mean = linear_with_mult(mult_tensors,
pi_latent_vector,
'pi',
self.size,
init_scale=init_scale,
init_bias=init_bias)
logstd = get_mult_variable(mult_tensors,
name="pi/logstd",
shape=[1, self.size],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
q_values = linear_with_mult(mult_tensors,
vf_latent_vector,
'q',
self.size,
init_scale=init_scale,
init_bias=init_bias)
return self.proba_distribution_from_flat(pdparam), mean, q_values
else:
mean = linear(pi_latent_vector,
'pi',
self.size,
init_scale=init_scale,
init_bias=init_bias)
policy.policy_neurons.append(mean)
logstd = tf.get_variable(name='pi/logstd',
shape=[1, self.size],
initializer=tf.zeros_initializer())
policy.policy_neurons.append(logstd)
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
q_values = linear(vf_latent_vector,
'q',
self.size,
init_scale=init_scale,
init_bias=init_bias)
return self.proba_distribution_from_flat(pdparam), mean, q_values
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=False,
**kwargs):
super(CustomWPPolicy, self).__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=reuse,
scale=False)
with tf.variable_scope("model", reuse=reuse):
activ = tf.nn.tanh
measurement_features = tf.expand_dims(self.processed_obs[:, -1],
axis=1)
measurement_features_flat = tf.layers.flatten(measurement_features)
pi_h = activ(
linear(measurement_features_flat,
"pi_vae_fc",
64,
init_scale=np.sqrt(2)))
pi_latent = activ(linear(pi_h, "pi_fc", 64, init_scale=np.sqrt(2)))
vf_h = activ(
linear(measurement_features_flat,
"vf_vae_fc",
64,
init_scale=np.sqrt(2)))
vf_latent = activ(linear(pi_h, "vf_fc", 64, init_scale=np.sqrt(2)))
value_fn = linear(vf_latent, 'vf', 1, init_scale=np.sqrt(2))
self._proba_distribution, self._policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(pi_latent, vf_latent, init_scale=0.01)
self._value_fn = value_fn
self._setup_init()
def vf_builder(vf_arch: str,
latent: tf.Tensor,
act_fun: tf.function,
shared_graph: GraphsTuple = None,
input_graph: GraphsTuple = None,
layer_size: int = 64,
layer_count: int = 3,
iterations: int = 10) -> tf.Tensor:
"""
Builds the value function network for
Args:
vf_arch: arch to use as a string
latent: the observation input
act_fun: activation function
shared_graph: the gnn output from the policy
input_graph: GraphTuple before any processing
iterations: number of iterations of message passing
Returns:
A tensor which will hold the value
"""
if vf_arch == "shared":
output_globals_vf = tf.reshape(shared_graph.globals, [-1, layer_size])
latent_vf = output_globals_vf
latent_vf = act_fun(
linear(latent_vf, "vf_fc0", 128, init_scale=np.sqrt(2)))
latent_vf = act_fun(
linear(latent_vf, "vf_fc1", 128, init_scale=np.sqrt(2)))
elif vf_arch == "graph":
model_vf = DDRGraphNetwork(layer_size=layer_size)
output_graph_vf = model_vf(input_graph, iterations)
output_globals_vf = tf.reshape(output_graph_vf.globals,
[-1, layer_size])
latent_vf = output_globals_vf
elif vf_arch == "mlp":
latent_vf = latent
latent_vf = act_fun(
linear(latent_vf, "vf_fc0", 128, init_scale=np.sqrt(2)))
latent_vf = act_fun(
linear(latent_vf, "vf_fc1", 128, init_scale=np.sqrt(2)))
latent_vf = act_fun(
linear(latent_vf, "vf_fc2", 128, init_scale=np.sqrt(2)))
else:
raise Exception("No such vf network")
return latent_vf
def my_small_cnn(scaled_images, **kwargs):
activ = tf.nn.relu
layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=3,
stride=1, **kwargs))
layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=3,
stride=1, **kwargs))
layer_3 = conv_to_fc(layer_2)
return activ(
linear(layer_3, 'fc1', n_hidden=32, init_scale=np.sqrt(2)))
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=False,
layers=None,
net_arch=None,
act_fun=tf.tanh,
cnn_extractor=None,
feature_extraction="cnn",
**kwargs):
super(CustomFeedForwardPolicy, self).__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=reuse,
scale=False)
self._kwargs_check(feature_extraction, kwargs)
with tf.variable_scope("model", reuse=reuse):
if feature_extraction == "cnn":
pi_latent = vf_latent = cnn_extractor(self.processed_obs,
**kwargs)
else:
Exception("nope")
assert str(
type(self.pdtype)
) == "<class 'stable_baselines.common.distributions.DiagGaussianProbabilityDistributionType'>"
self._value_fn = linear(vf_latent, 'vf', 1)
self._proba_distribution, self._policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(
pi_latent, vf_latent, init_scale=0.01)
# self._value_fn = linear(vf_latent, 'vf', 1)
# mean = pi_latent
# n_jets = mean.shape[1]
# print("njets", n_jets)
# logstd = tf.get_variable(name='pi/logstd', shape=[1, n_jets], initializer=tf.zeros_initializer())
# pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
# # we take the last layer of our cnn for policy and q value
# self._proba_distribution = self.pdtype.proba_distribution_from_flat(pdparam)
# self._policy = pi_latent
# self.q_value = vf_latent
# print("heyyyyyyy")
self._setup_init()
def attention_cnn(scaled_images, **kwargs):
"""Nature CNN with region-sensitive module"""
def softmax_2d(tensor):
b, h, w, c = tensor.shape
tensor = tf.reshape(tensor, (-1, h * w, c))
tensor = tf.nn.softmax(tensor, axis=1)
tensor = tf.reshape(tensor, (-1, h, w, c))
return tensor
c1 = tf.nn.relu(
conv(scaled_images,
'c1',
n_filters=32,
filter_size=8,
stride=4,
init_scale=np.sqrt(2),
**kwargs))
c2 = tf.nn.relu(
conv(c1,
'c2',
n_filters=64,
filter_size=4,
stride=2,
init_scale=np.sqrt(2),
**kwargs))
c3 = tf.nn.relu(
conv(c2,
'c3',
n_filters=64,
filter_size=3,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
c3 = tf.nn.l2_normalize(c3, axis=-1)
a1 = tf.nn.elu(
conv(c3,
'a1',
n_filters=512,
filter_size=1,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
a2 = softmax_2d(
conv(a1,
'a2',
n_filters=2,
filter_size=1,
stride=1,
init_scale=np.sqrt(2),
**kwargs))
a2 = tf.identity(a2, name='attn')
x = c3 * tf.reduce_sum(a2, axis=-1, keepdims=True)
x = conv_to_fc(x)
return tf.nn.relu(linear(x, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))
def proba_distribution_from_latent(self,
pi_latent_vector,
vf_latent_vector,
action_mask_vector=None,
init_scale=1.0,
init_bias=0.0):
pdparam = linear(pi_latent_vector,
'pi',
sum(self.n_vec),
init_scale=init_scale,
init_bias=init_bias)
q_values = linear(vf_latent_vector,
'q',
sum(self.n_vec),
init_scale=init_scale,
init_bias=init_bias)
return self.proba_distribution_from_flat(
pdparam, action_mask_vector=action_mask_vector), pdparam, q_values
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
reuse=False,
layers=None,
cnn_extractor=nature_cnn,
feature_extraction="cnn",
obs_phs=None,
layer_norm=False,
**kwargs):
super(FeedForwardPolicy,
self).__init__(sess,
ob_space,
ac_space,
n_env,
n_steps,
n_batch,
n_lstm=256,
reuse=reuse,
scale=(feature_extraction == "cnn"),
obs_phs=obs_phs)
if layers is None:
layers = [64, 64]
with tf.variable_scope("model", reuse=reuse):
if feature_extraction == "cnn":
extracted_features = cnn_extractor(self.processed_x, **kwargs)
pi_latent = extracted_features
else:
activ = tf.nn.relu
processed_x = tf.layers.flatten(self.processed_x)
pi_h = processed_x
for i, layer_size in enumerate(layers):
pi_h = linear(pi_h,
'pi_fc' + str(i),
n_hidden=layer_size,
init_scale=np.sqrt(2))
if layer_norm:
pi_h = tf.contrib.layers.layer_norm(pi_h,
center=True,
scale=True)
pi_h = activ(pi_h)
pi_latent = pi_h
self.proba_distribution, self.policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(pi_latent, pi_latent, init_scale=0.01)
self.value_fn = self.policy
self.initial_state = None
self._setup_init()
def cnn_extractor(scaled_images, channels=c, w=w, h=h):
print(f"========= REAL SHAPE: {scaled_images.shape} ===========")
original_shape = scaled_images.shape[1]
print(f"========= SHAPE: {original_shape} ===========")
scaled_images = tf.reshape(scaled_images, (-1, h, w, channels))
activ = tf.nn.relu
layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=w, stride=1, init_scale=np.sqrt(2)))
layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=1, stride=1, init_scale=np.sqrt(2)))
layer_3 = activ(conv(layer_2, 'c3', n_filters=128, filter_size=1, stride=1, init_scale=np.sqrt(2)))
layer_3 = conv_to_fc(layer_3)
return activ(linear(layer_3, 'fc1', n_hidden=128, init_scale=np.sqrt(2)))
def proba_distribution_from_latent(self,
pi_latent_vector,
vf_latent_vector,
init_scale=1.0,
init_bias=0.0):
mean = linear(pi_latent_vector,
'pi',
self.size,
init_scale=init_scale,
init_bias=init_bias)
logstd = tf.get_variable(name='pi/logstd',
shape=[1, self.size],
initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
q_values = linear(vf_latent_vector,
'q',
self.size,
init_scale=init_scale,
init_bias=init_bias)
return self.proba_distribution_from_flat(pdparam), mean, q_values
def build_actor_critic_network_actionsadded(x, layers, action_indices,
state_indices, reuse):
activ = tf.nn.relu
with tf.variable_scope("actor_critic", reuse=tf.AUTO_REUSE):
actions = tf.gather(x, action_indices, axis=1)
actions = tf.reduce_sum(actions, axis=1, keepdims=True)
state = tf.gather(x, state_indices, axis=1)
vf_h = tf.layers.flatten(tf.concat([actions, state], axis=1))
for j, layer_size in enumerate(layers):
vf_h = activ(
linear(vf_h,
'vf_fc' + str(j),
n_hidden=layer_size,
init_scale=np.sqrt(2)))
vf_latent = activ(linear(vf_h, 'vf_head', len(action_indices)))
value_fn = linear(vf_latent, 'vf', 1)
pi_latent = build_policy(x, layers, action_indices, state_indices, activ)
return pi_latent, vf_latent, value_fn
