'''
from baselines/ppo1/mlp_policy.py and add simple modification
(1) add reuse argument
(2) cache the `stochastic` placeholder
'''
import tensorflow as tf
import gym
import baselines.common.tf_util as U
from baselines.common.mpi_running_mean_std import RunningMeanStd
from baselines.common.distributions import make_pdtype
from baselines.acktr.utils import dense
class MlpPolicy(object):
recurrent = False
def __init__(self, name, reuse=False, *args, **kwargs):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
self._init(*args, **kwargs)
self.scope = tf.get_variable_scope().name
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "vffc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
self.vpred = dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "polfc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
# change for BC
stochastic = U.get_placeholder(name="stochastic", dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = ac
self._act = U.function([stochastic, ob], [ac, self.vpred])
def act(self, stochastic, ob):
ac1, vpred1 = self._act(stochastic, ob[None])
return ac1[0], vpred1[0]
def get_variables(self):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope)
def get_trainable_variables(self):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)
def get_initial_state(self):
return []
