baselines/gail/mlp_policy.py [22:34]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            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)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



baselines/ppo1/mlp_policy.py [11:23]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            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)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -