def __init__(self,
observation_space,
action_space,
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(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(action_space, gym.spaces.Discrete)
self.q = fc(vf_latent, 'q', action_space.n)
self.vf = self.q
else:
self.vf = fc(vf_latent, 'vf', 1)
self.vf = self.vf[:, 0]
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 _matching_fc(tensor, name, size, init_scale, init_bias):
if tensor.shape[-1] == size:
return tensor
else:
return fc(tensor,
name,
size,
init_scale=init_scale,
init_bias=init_bias)
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 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