import tensorflow as tf

    Tuple (action logits, value).
  """
  if not isinstance(action_space, gym.spaces.Discrete):
    raise ValueError("Expecting discrete action space.")

  obs_shape = common_layers.shape_list(observations)
  (frame_height, frame_width) = obs_shape[2:4]

  # TODO(afrozm): We have these dummy problems mainly for hparams, so cleanup
  # when possible and do this properly.
  if hparams.policy_problem_name == "dummy_policy_problem_ttt":
    tf.logging.info("Using DummyPolicyProblemTTT for the policy.")
    policy_problem = tic_tac_toe_env.DummyPolicyProblemTTT()
  else:
    tf.logging.info("Using DummyPolicyProblem for the policy.")
    policy_problem = DummyPolicyProblem(action_space, frame_height, frame_width)

  trainer_lib.add_problem_hparams(hparams, policy_problem)
  hparams.force_full_predict = True
  model = registry.model(hparams.policy_network)(
      hparams, tf.estimator.ModeKeys.TRAIN
  )
