def build_act(make_obs_ph, q_func, num_actions, scope='deepq', reuse=None):
    'Creates the act function:\n\n    Parameters\n    ----------\n    make_obs_ph: str -> tf.compat.v1.placeholder or TfInput\n        a function that take a name and creates a placeholder of input with that name\n    q_func: (tf.Variable, int, str, bool) -> tf.Variable\n        the model that takes the following inputs:\n            observation_in: object\n                the output of observation placeholder\n            num_actions: int\n                number of actions\n            scope: str\n            reuse: bool\n                should be passed to outer variable scope\n        and returns a tensor of shape (batch_size, num_actions) with values of every action.\n    num_actions: int\n        number of actions.\n    scope: str or VariableScope\n        optional scope for variable_scope.\n    reuse: bool or None\n        whether or not the variables should be reused. To be able to reuse the scope must be given.\n\n    Returns\n    -------\n    act: (tf.Variable, bool, float) -> tf.Variable\n        function to select and action given observation.\n`       See the top of the file for details.\n    '
    with tf.compat.v1.variable_scope(scope, reuse=reuse):
        observations_ph = make_obs_ph('observation')
        stochastic_ph = tf.compat.v1.placeholder(tf.bool, (), name='stochastic')
        update_eps_ph = tf.compat.v1.placeholder(tf.float32, (), name='update_eps')
        eps = tf.compat.v1.get_variable('eps', (), initializer=tf.constant_initializer(0))
        q_values = q_func(observations_ph.get(), num_actions, scope='q_func')
        deterministic_actions = tf.argmax(q_values, axis=1)
        batch_size = tf.shape(observations_ph.get())[0]
        random_actions = tf.compat.v1.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
        chose_random = (tf.compat.v1.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps)
        stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
        output_actions = tf.cond(stochastic_ph, (lambda : stochastic_actions), (lambda : deterministic_actions))
        update_eps_expr = eps.assign(tf.cond((update_eps_ph >= 0), (lambda : update_eps_ph), (lambda : eps)))
        _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph], outputs=output_actions, givens={update_eps_ph: (- 1.0), stochastic_ph: True}, updates=[update_eps_expr])

        def act(ob, stochastic=True, update_eps=(- 1)):
            return _act(ob, stochastic, update_eps)
        return act
Creates the act function:

    Parameters
    ----------
    make_obs_ph: str -> tf.compat.v1.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.
def build_act(make_obs_ph, q_func, num_actions, scope='deepq', reuse=None):
    'Creates the act function:\n\n    Parameters\n    ----------\n    make_obs_ph: str -> tf.compat.v1.placeholder or TfInput\n        a function that take a name and creates a placeholder of input with that name\n    q_func: (tf.Variable, int, str, bool) -> tf.Variable\n        the model that takes the following inputs:\n            observation_in: object\n                the output of observation placeholder\n            num_actions: int\n                number of actions\n            scope: str\n            reuse: bool\n                should be passed to outer variable scope\n        and returns a tensor of shape (batch_size, num_actions) with values of every action.\n    num_actions: int\n        number of actions.\n    scope: str or VariableScope\n        optional scope for variable_scope.\n    reuse: bool or None\n        whether or not the variables should be reused. To be able to reuse the scope must be given.\n\n    Returns\n    -------\n    act: (tf.Variable, bool, float) -> tf.Variable\n        function to select and action given observation.\n`       See the top of the file for details.\n    '
    with tf.compat.v1.variable_scope(scope, reuse=reuse):
        observations_ph = make_obs_ph('observation')
        stochastic_ph = tf.compat.v1.placeholder(tf.bool, (), name='stochastic')
        update_eps_ph = tf.compat.v1.placeholder(tf.float32, (), name='update_eps')
        eps = tf.compat.v1.get_variable('eps', (), initializer=tf.constant_initializer(0))
        q_values = q_func(observations_ph.get(), num_actions, scope='q_func')
        deterministic_actions = tf.argmax(q_values, axis=1)
        batch_size = tf.shape(observations_ph.get())[0]
        random_actions = tf.compat.v1.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
        chose_random = (tf.compat.v1.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps)
        stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
        output_actions = tf.cond(stochastic_ph, (lambda : stochastic_actions), (lambda : deterministic_actions))
        update_eps_expr = eps.assign(tf.cond((update_eps_ph >= 0), (lambda : update_eps_ph), (lambda : eps)))
        _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph], outputs=output_actions, givens={update_eps_ph: (- 1.0), stochastic_ph: True}, updates=[update_eps_expr])

        def act(ob, stochastic=True, update_eps=(- 1)):
            return _act(ob, stochastic, update_eps)
        return act