def build_act(make_obs_ph, q_func, num_actions, scope="<STR_LIT>", reuse=None):
with tf.variable_scope(scope, reuse=reuse):<EOL><INDENT>observations_ph = make_obs_ph("<STR_LIT>")<EOL>stochastic_ph = tf.placeholder(tf.bool, (), name="<STR_LIT>")<EOL>update_eps_ph = tf.placeholder(tf.float32, (), name="<STR_LIT>")<EOL>eps = tf.get_variable("<STR_LIT>", (), initializer=tf.constant_initializer(<NUM_LIT:0>))<EOL>q_values = q_func(observations_ph.get(), num_actions, scope="<STR_LIT>")<EOL>deterministic_actions = tf.argmax(q_values, axis=<NUM_LIT:1>)<EOL>batch_size = tf.shape(observations_ph.get())[<NUM_LIT:0>]<EOL>random_actions = tf.random_uniform(tf.stack([batch_size]), minval=<NUM_LIT:0>, maxval=num_actions, dtype=tf.int64)<EOL>chose_random = tf.random_uniform(tf.stack([batch_size]), minval=<NUM_LIT:0>, maxval=<NUM_LIT:1>, dtype=tf.float32) < eps<EOL>stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)<EOL>output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)<EOL>update_eps_expr = eps.assign(tf.cond(update_eps_ph >= <NUM_LIT:0>, lambda: update_eps_ph, lambda: eps))<EOL>_act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],<EOL>outputs=output_actions,<EOL>givens={update_eps_ph: -<NUM_LIT:1.0>, stochastic_ph: True},<EOL>updates=[update_eps_expr])<EOL>def act(ob, stochastic=True, update_eps=-<NUM_LIT:1>):<EOL><INDENT>return _act(ob, stochastic, update_eps)<EOL><DEDENT>return act<EOL><DEDENT>
Creates the act function:

    Parameters
    ----------
    make_obs_ph: str -> tf.placeholder or TfInput
        a function that take a name and creates a placeholder of input with that name
    q_func: (tf.Variable, int, str, bool) -> tf.Variable
        the model that takes the following inputs:
            observation_in: object
                the output of observation placeholder
            num_actions: int
                number of actions
            scope: str
            reuse: bool
                should be passed to outer variable scope
        and returns a tensor of shape (batch_size, num_actions) with values of every action.
    num_actions: int
        number of actions.
    scope: str or VariableScope
        optional scope for variable_scope.
    reuse: bool or None
        whether or not the variables should be reused. To be able to reuse the scope must be given.

    Returns
    -------
    act: (tf.Variable, bool, float) -> tf.Variable
        function to select and action given observation.
`       See the top of the file for details.