def policy(obs, dimA, reuse=False):
with tf.variable_scope('policy', reuse=reuse):
h1 = tf.nn.relu(linear(obs, dout=l1, name='h1'))
h2 = tf.nn.relu(linear(h1, dout=l2, name='h2'))
h3 = tf.identity(linear(h2, dout=dimA), name='h3')
action = tf.nn.tanh(h3, name='h4-action') * action_scale
return action
def qfunction(obs, act, reuse=False):
with tf.variable_scope('qfunc', reuse=reuse):
h1 = tf.nn.relu(linear(obs, dout=l1, name='h1'))
h1a = tf.concat(1, [h1, act])
h2 = tf.nn.relu(linear(h1a, dout=l2, name='h2'))
qs = linear(h2, dout=1)
q = tf.squeeze(qs, [1], name='h3-q')
return q
def inner(obs, dU, reuse=False):
out = obs
with tf.variable_scope('policy', reuse=reuse):
for i in range(num_hidden):
out = tf.nn.relu(
linear(out, dout=dim_hidden, name='layer_%d' % i))
out = linear(out, dout=dU, init_scale=0.01)
pol = tf.nn.tanh(out) * (diff / 2) + mid
return pol
def compute_params_tensor(self, logit):
mu = linear(logit, dout=self.dU, name='mu')
sigma = tf.exp(linear(logit, dout=self.dU, name='logsig'))
#params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=vs.name)
if self.min_var > 0:
sigma = tf.maximum(self.min_var, sigma)
if self.mean_clamp:
mu = tf.nn.tanh(mu) * self.mean_clamp
dist_params = [mu, sigma]
self.params = dist_params
def inner(state, action, reuse=False):
sout = state
dU = int(action.get_shape()[1])
with tf.variable_scope('q_function', reuse=reuse) as vs:
for i in range(num_hidden):
sout = tf.nn.relu(
linear(sout, dout=dim_hidden, name='layer_%d' % i))
sa = tf.concat(1, [sout, action])
assert_shape(sa, [None, dim_hidden + dU])
out = tf.nn.relu(linear(sa, dout=dim_hidden, name='sa1'))
out = linear(out, dout=1, init_scale=0.01, name='sa2')
return out
def inner(obs, dU, reuse=False):
out = obs
dist = DiagGauss(dU, mean_clamp=mean_clamp, min_var=min_std)
with tf.variable_scope('policy', reuse=reuse) as vs:
for i in range(num_hidden):
out = tf.nn.relu(
linear(out, dout=dim_hidden, name='layer_%d' % i))
dist.compute_params_tensor(out)
return dist
def linear_value_fn(state):
value = linear(state, dout=1)
return value
def linear_q_fn(state, action, reuse=False):
with tf.variable_scope('q_function', reuse=reuse):
a1 = linear(state, dout=1, name='state')
a2 = linear(action, dout=1, name='act')
return a1 + a2
def compute_params_tensor(self, logit):
probs = tf.nn.softmax(linear(logit, dout=self.dU))
self.params = [probs]
