Release 6 fix nan (#4343)

* test initalize steps to 100

* use mean of first trajectory to initialize the normalizer

* remove blank line

* update changelog

* cleaned up initialization of variance/mean

* large normalization obs unit test

* add --upgrade to pip to get newer downloader (#4338)

* Fix format of the changelog for validation. (#4340)

Co-authored-by: Chris Elion <chris.elion@unity3d.com>
Co-authored-by: Chris Goy <christopherg@unity3d.com>

.yamato/com.unity.ml-agents-performance.yml

   variables:
     UNITY_VERSION: {{ editor.version }}
   commands:
-    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple
+    - python -m pip install unity-downloader-cli --index-url https://artifactory.prd.it.unity3d.com/artifactory/api/pypi/pypi/simple --upgrade
     - unity-downloader-cli -u {{ editor.version }} -c editor --wait --fast
     - curl -s https://artifactory.internal.unity3d.com/core-automation/tools/utr-standalone/utr --output utr
     - chmod +x ./utr

com.unity.ml-agents/CHANGELOG.md

 Previously, this would result in an infinite loop and cause the editor to hang.
 (#4226)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
+- The algorithm used to normalize observations was introducing NaNs if the initial observations were too large
+due to incorrect initialization. The initialization was fixed and is now the observation means from the
+first trajectory processed. (#4299)
 
 ## [1.2.0-preview] - 2020-07-15
 

ml-agents/mlagents/trainers/policy/tf_policy.py

         self.assign_ops: List[tf.Operation] = []
         self.update_dict: Dict[str, tf.Tensor] = {}
         self.inference_dict: Dict[str, tf.Tensor] = {}
+        self.first_normalization_update: bool = False
 
         self.graph = tf.Graph()
         self.sess = tf.Session(
         :param vector_obs: The vector observations to add to the running estimate of the distribution.
         """
         if self.use_vec_obs and self.normalize:
-            self.sess.run(
-                self.update_normalization_op, feed_dict={self.vector_in: vector_obs}
-            )
+            if self.first_normalization_update:
+                self.sess.run(
+                    self.init_normalization_op, feed_dict={self.vector_in: vector_obs}
+                )
+                self.first_normalization_update = False
+            else:
+                self.sess.run(
+                    self.update_normalization_op, feed_dict={self.vector_in: vector_obs}
+                )
 
     @property
     def use_vis_obs(self):
         self.normalization_steps: Optional[tf.Variable] = None
         self.running_mean: Optional[tf.Variable] = None
         self.running_variance: Optional[tf.Variable] = None
+        self.init_normalization_op: Optional[tf.Operation] = None
         self.update_normalization_op: Optional[tf.Operation] = None
         self.value: Optional[tf.Tensor] = None
         self.all_log_probs: tf.Tensor = None
                 self.behavior_spec.observation_shapes
             )
             if self.normalize:
+                self.first_normalization_update = True
+                self.init_normalization_op = normalization_tensors.init_op
                 self.normalization_steps = normalization_tensors.steps
                 self.running_mean = normalization_tensors.running_mean
                 self.running_variance = normalization_tensors.running_variance

ml-agents/mlagents/trainers/tests/mock_brain.py

             memory=memory,
         )
         steps_list.append(experience)
+    obs = []
+    for _shape in observation_shapes:
+        obs.append(np.ones(_shape, dtype=np.float32))
     last_experience = AgentExperience(
         obs=obs,
         reward=reward,

ml-agents/mlagents/trainers/tests/test_nn_policy.py

 DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
 BUFFER_INIT_SAMPLES = 32
 NUM_AGENTS = 12
+EPSILON = 1e-7
 
 
 def create_policy_mock(
         assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE)
 
 
+def test_large_normalization():
+    behavior_spec = mb.setup_test_behavior_specs(
+        use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
+    )
+    # Taken from Walker seed 3713 which causes NaN without proper initialization
+    large_obs1 = [
+        1800.00036621,
+        1799.96972656,
+        1800.01245117,
+        1800.07214355,
+        1800.02758789,
+        1799.98303223,
+        1799.88647461,
+        1799.89575195,
+        1800.03479004,
+        1800.14025879,
+        1800.17675781,
+        1800.20581055,
+        1800.33740234,
+        1800.36450195,
+        1800.43457031,
+        1800.45544434,
+        1800.44604492,
+        1800.56713867,
+        1800.73901367,
+    ]
+    large_obs2 = [
+        1799.99975586,
+        1799.96679688,
+        1799.92980957,
+        1799.89550781,
+        1799.93774414,
+        1799.95300293,
+        1799.94067383,
+        1799.92993164,
+        1799.84057617,
+        1799.69873047,
+        1799.70605469,
+        1799.82849121,
+        1799.85095215,
+        1799.76977539,
+        1799.78283691,
+        1799.76708984,
+        1799.67163086,
+        1799.59191895,
+        1799.5135498,
+        1799.45556641,
+        1799.3717041,
+    ]
+    policy = TFPolicy(
+        0,
+        behavior_spec,
+        TrainerSettings(network_settings=NetworkSettings(normalize=True)),
+        "testdir",
+        False,
+    )
+    time_horizon = len(large_obs1)
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_shapes=[(1,)],
+        action_space=[2],
+    )
+    for i in range(time_horizon):
+        trajectory.steps[i].obs[0] = np.array([large_obs1[i]], dtype=np.float32)
+    trajectory_buffer = trajectory.to_agentbuffer()
+    policy.update_normalization(trajectory_buffer["vector_obs"])
+
+    # Check that the running mean and variance is correct
+    steps, mean, variance = policy.sess.run(
+        [policy.normalization_steps, policy.running_mean, policy.running_variance]
+    )
+    assert mean[0] == pytest.approx(np.mean(large_obs1, dtype=np.float32), abs=0.01)
+    assert variance[0] / steps == pytest.approx(
+        np.var(large_obs1, dtype=np.float32), abs=0.01
+    )
+
+    time_horizon = len(large_obs2)
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_shapes=[(1,)],
+        action_space=[2],
+    )
+    for i in range(time_horizon):
+        trajectory.steps[i].obs[0] = np.array([large_obs2[i]], dtype=np.float32)
+
+    trajectory_buffer = trajectory.to_agentbuffer()
+    policy.update_normalization(trajectory_buffer["vector_obs"])
+
+    steps, mean, variance = policy.sess.run(
+        [policy.normalization_steps, policy.running_mean, policy.running_variance]
+    )
+
+    assert mean[0] == pytest.approx(
+        np.mean(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
+    )
+    assert variance[0] / steps == pytest.approx(
+        np.var(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
+    )
+
+
-
     time_horizon = 6
     trajectory = make_fake_trajectory(
         length=time_horizon,
 
     assert steps == 6
     assert mean[0] == 0.5
-    # Note: variance is divided by number of steps, and initialized to 1 to avoid
-    # divide by 0. The right answer is 0.25
-    assert (variance[0] - 1) / steps == 0.25
-
+    # Note: variance is initalized to the variance of the initial trajectory + EPSILON
+    # (to avoid divide by 0) and multiplied by the number of steps. The correct answer is 0.25
+    assert variance[0] / steps == pytest.approx(0.25, abs=0.01)
     # Make another update, this time with all 1's
     time_horizon = 10
     trajectory = make_fake_trajectory(
 
     assert steps == 16
     assert mean[0] == 0.8125
-    assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01)
+    assert variance[0] / steps == pytest.approx(0.152, abs=0.01)
 
 
 def test_min_visual_size():

ml-agents/mlagents/trainers/tf/models.py

 
 
 class NormalizerTensors(NamedTuple):
+    init_op: tf.Operation
     update_op: tf.Operation
     steps: tf.Tensor
     running_mean: tf.Tensor
         :return: A NormalizerTensors tuple that holds running mean, running variance, number of steps,
             and the update operation.
         """
-
+
         steps = tf.get_variable(
             "normalization_steps",
             [],
             dtype=tf.float32,
             initializer=tf.ones_initializer(),
         )
-        update_normalization = ModelUtils.create_normalizer_update(
+        initialize_normalization, update_normalization = ModelUtils.create_normalizer_update(
-            update_normalization, steps, running_mean, running_variance
+            initialize_normalization,
+            update_normalization,
+            steps,
+            running_mean,
+            running_variance,
         )
 
     @staticmethod
         running_mean: tf.Tensor,
         running_variance: tf.Tensor,
-    ) -> tf.Operation:
+    ) -> Tuple[tf.Operation, tf.Operation]:
         """
         Creates the update operation for the normalizer.
         :param vector_input: Vector observation to use for updating the running mean and variance.
         update_mean = tf.assign(running_mean, new_mean)
         update_variance = tf.assign(running_variance, new_variance)
         update_norm_step = tf.assign(steps, total_new_steps)
-        return tf.group([update_mean, update_variance, update_norm_step])
+        # First mean and variance calculated normally
+        initial_mean, initial_variance = tf.nn.moments(vector_input, axes=[0])
+        initialize_mean = tf.assign(running_mean, initial_mean)
+        # Multiplied by total_new_step because it is divided by total_new_step in the normalization
+        initialize_variance = tf.assign(
+            running_variance,
+            (initial_variance + EPSILON) * tf.cast(total_new_steps, dtype=tf.float32),
+        )
+        return (
+            tf.group([initialize_mean, initialize_variance, update_norm_step]),
+            tf.group([update_mean, update_variance, update_norm_step]),
+        )
 
     @staticmethod
     def create_vector_observation_encoder(