import tensorflow as tf

    criterion = criterion_map[FLAGS.model.loss]
    loss_mod = MultiStepLoss(model, normalizers, dim_state, dim_action, criterion, FLAGS.model.multi_step)
    loss_mod.build_backward(FLAGS.model.lr, FLAGS.model.weight_decay)
    shadow_loss_mods = [MultiStepLoss(shadow_model, normalizers, dim_state, dim_action, criterion, FLAGS.model.multi_step) for shadow_model in shadow_models]
    for shadow_loss_mod in shadow_loss_mods:
        shadow_loss_mod.build_backward(FLAGS.model.lr, FLAGS.model.weight_decay)
    algo = TRPO(vfn=vfn, policy=policy, dim_state=dim_state, dim_action=dim_action, **FLAGS.TRPO.as_dict())
    advtask = ADVTASK(dim_state, dim_action, policy, vfn, warmup_policy, warmup_vfn, task, alpha=alpha, beta=beta, nsample=nsample, atype=atype)
    tf.get_default_session().run(tf.global_variables_initializer())

    print ("norm params:", normalizers_parameters)
    print ("norm_copy params:", normalizers_copy_parameters)
    norm_before = tf.get_default_session().run(normalizers_parameters)
    print ("norm_before:", norm_before)

    assert FLAGS.algorithm != 'MF', "don't support model free for now"
