def __init__(self,
env,
observations,
latent,
estimate_q=False,
vf_latent=None,
sess=None,
**tensors):
"""
Parameters:
----------
env RL environment
observations tensorflow placeholder in which the observations will be fed
latent latent state from which policy distribution parameters should be inferred
vf_latent latent state from which value function should be inferred (if None, then latent is used)
sess tensorflow session to run calculations in (if None, default session is used)
**tensors tensorflow tensors for additional attributes such as state or mask
"""
self.X = observations
self.state = tf.constant([])
self.initial_state = None
self.__dict__.update(tensors)
vf_latent = vf_latent if vf_latent is not None else latent
vf_latent = tf.layers.flatten(vf_latent)
latent = tf.layers.flatten(latent)
# Based on the action space, will select what probability distribution type
self.pdtype = make_pdtype(env.action_space)
self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01)
# Take an action
self.action = self.pd.sample()
# Calculate the neg log of our probability
self.neglogp = self.pd.neglogp(self.action)
self.sess = sess or tf.get_default_session()
if estimate_q:
assert isinstance(env.action_space, gym.spaces.Discrete)
self.q = fc(vf_latent, 'q', env.action_space.n)
self.vf = self.q
else:
self.vf = fc(vf_latent, 'vf', 1)
self.vf = self.vf[:, 0]
#Lyapunov
self.Lyapunov = fc(vf_latent, 'vf', 1)
self.Lyapunov = self.Lyapunov[:, 0]
def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
pdparam = fc(latent_vector,
'pi',
self.ncat,
init_scale=init_scale,
init_bias=init_bias)
return self.pdfromflat(pdparam), pdparam
def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(
conv(scaled_images,
'c1',
nf=32,
rf=8,
stride=4,
init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(
conv(h,
'c2',
nf=64,
rf=4,
stride=2,
init_scale=np.sqrt(2),
**conv_kwargs))
h3 = activ(
conv(h2,
'c3',
nf=64,
rf=3,
stride=1,
init_scale=np.sqrt(2),
**conv_kwargs))
h3 = conv_to_fc(h3)
return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
def network_fn(X):
h = tf.cast(X, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(h, 'c1', nf=8, rf=8, stride=4, init_scale=np.sqrt(2), **conv_kwargs))
h = activ(conv(h, 'c2', nf=16, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
h = conv_to_fc(h)
h = activ(fc(h, 'fc1', nh=128, init_scale=np.sqrt(2)))
return h
def network_fn(X):
h = tf.layers.flatten(X)
for i in range(num_layers):
h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
if layer_norm:
h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
h = activation(h)
return h
def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
mean = fc(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)
return self.pdfromflat(pdparam), mean
def network_fn(X):
num_layers = 4
num_hidden = 100
# activation=tf.tanh
activation = tf.nn.relu
layer_norm = False
h = tf.layers.flatten(X)
for i in range(num_layers):
h = fc(h,
'mlp_fc{}'.format(i),
nh=num_hidden,
init_scale=np.sqrt(2),
trainable=True)
if layer_norm:
h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
h = activation(h)
return h
