How to use the openai.deepq function in openai

prioritized_replay_beta0, prioritized_replay_beta_iters,
        prioritized_replay_eps, experiment_name, load_path, network_kwargs):
    env = DotaEnvironment()

    sess = get_session()
    set_global_seeds(seed)

    q_func = build_q_func(network, **network_kwargs)

    # capture the shape outside the closure so that the env object is not serialized
    # by cloudpickle when serializing make_obs_ph
    observation_space = env.observation_space
    def make_obs_ph(name):
        return ObservationInput(observation_space, name=name)

    act, _, _, debug = deepq.build_train(
        scope='deepq_act',
        make_obs_ph=make_obs_ph,
        q_func=q_func,
        num_actions=env.action_space.n,
        optimizer=tf.train.AdamOptimizer(learning_rate=lr),
        gamma=gamma,
        grad_norm_clipping=10, )

    act_params = {
        'make_obs_ph': make_obs_ph,
        'q_func': q_func,
        'num_actions': env.action_space.n, }

    act = ActWrapper(act, act_params)

    exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),