mlp_policy.py 文件源码

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)
        # Kiwoo: Currently block MPI, need to consider entire code in here 

        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(U.dense(last_out, hid_size, "vffc%i"%(i+1), weight_init=U.normc_initializer(1.0)))
        self.vpred = U.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(U.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 = U.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 = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
        else:
            pdparam = U.dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))

        self.pd = pdtype.pdfromflat(pdparam)

        self.state_in = []
        self.state_out = []

        stochastic = tf.placeholder(dtype=tf.bool, shape=())
        ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
        self._act = U.function([stochastic, ob], [ac, self.vpred])