old_contents: # coding=utf-8
  # Copyright 2019 The Tensor2Tensor Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Tests for tensor2tensor.envs.mujoco_problems."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import mujoco_problems  # pylint: disable=unused-import
  from tensor2tensor.utils import registry
  import tensorflow as tf
  
  
  class ReacherEnvProblemTest(tf.test.TestCase):
  
    def test_registration_and_interaction_with_env_problem(self):
      batch_size = 5
      # This ensures that registration has occurred.
      ep = registry.env_problem("reacher_env_problem", batch_size=batch_size)
      ep.reset()
      num_done, num_lost, num_won, num_draw = 0, 0, 0, 0
      nsteps = 100
      for _ in range(nsteps):
        actions = np.stack([ep.action_space.sample() for _ in range(batch_size)])
        obs, rewards, dones, infos = ep.step(actions)
  
        # Assert that things are happening batchwise.
        self.assertEqual(batch_size, len(obs))
        self.assertEqual(batch_size, len(rewards))
        self.assertEqual(batch_size, len(dones))
        self.assertEqual(batch_size, len(infos))
  
        done_indices = env_problem_utils.done_indices(dones)
        ep.reset(done_indices)
        num_done += sum(dones)
        for r, d in zip(rewards, dones):
          if not d:
            continue
          if r == -1:
            num_lost += 1
          elif r == 0:
            num_draw += 1
          elif r == 1:
            num_won += 1
          else:
            raise ValueError("reward should be -1, 0, 1 but is {}".format(r))
  
      # Assert that something got done atleast, without that the next assert is
      # meaningless.
      self.assertGreater(num_done, 0)
  
      # Assert that things are consistent.
      self.assertEqual(num_done, num_won + num_lost + num_draw)
  
  
  if __name__ == "__main__":
    tf.test.main()
  
new_contents: # coding=utf-8
  # Copyright 2019 The Tensor2Tensor Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Tests for tensor2tensor.envs.mujoco_problems."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import mujoco_problems  # pylint: disable=unused-import
  from tensor2tensor.utils import registry
  import tensorflow as tf
  
  
  class ReacherEnvProblemTest(tf.test.TestCase):
  
    def test_registration_and_interaction_with_env_problem(self):
      batch_size = 5
      # This ensures that registration has occurred.
      ep = registry.env_problem("reacher_env_problem", batch_size=batch_size)
      ep.reset()
      num_done = 0
      nsteps = 100
      for _ in range(nsteps):
        actions = np.stack([ep.action_space.sample() for _ in range(batch_size)])
        obs, rewards, dones, infos = ep.step(actions)
  
        # Assert that things are happening batchwise.
        self.assertEqual(batch_size, len(obs))
        self.assertEqual(batch_size, len(rewards))
        self.assertEqual(batch_size, len(dones))
        self.assertEqual(batch_size, len(infos))
  
        done_indices = env_problem_utils.done_indices(dones)
        ep.reset(done_indices)
        num_done += sum(dones)
  
      # Assert that something got done atleast,
      self.assertGreater(num_done, 0)
  
  
  if __name__ == "__main__":
    tf.test.main()
  
subject: update the reacher env test.