use int64 steps, check for NaN actions (#4607) (#4654)

* use int64 steps

* check for NaN actions

Co-authored-by: Ruo-Ping Dong <ruoping.dong@unity3d.com>

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

com.unity.ml-agents/CHANGELOG.md

 Previously, this would result in an infinite loop and cause the editor to hang. (#4573)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 - Fixed an issue where runs could not be resumed when using TensorFlow and Ghost Training. (#4593)
+- Change the tensor type of step count from int32 to int64 to address the overflow issue when step
+goes larger than 2^31. Previous Tensorflow checkpoints will become incompatible and cannot be loaded. (#4607)
 
 
 ## [1.5.0-preview] - 2020-10-14

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

     ) -> ActionInfo:
         raise NotImplementedError
 
+    @staticmethod
+    def check_nan_action(action: Optional[np.ndarray]) -> None:
+        # Fast NaN check on the action
+        # See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background.
+        if action is not None:
+            d = np.sum(action)
+            has_nan = np.isnan(d)
+            if has_nan:
+                raise RuntimeError("NaN action detected.")
+
     @abstractmethod
     def update_normalization(self, vector_obs: np.ndarray) -> None:
         pass

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

         )
 
         self.save_memories(global_agent_ids, run_out.get("memory_out"))
+        self.check_nan_action(run_out.get("action"))
+
         return ActionInfo(
             action=run_out.get("action"),
             value=run_out.get("value"),

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

             decision_requests, global_agent_ids
         )  # pylint: disable=assignment-from-no-return
         self.save_memories(global_agent_ids, run_out.get("memory_out"))
+        self.check_nan_action(run_out.get("action"))
         return ActionInfo(
             action=run_out.get("action"),
             value=run_out.get("value"),

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

                 enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False)
 
 
+def test_step_overflow():
+    behavior_spec = mb.setup_test_behavior_specs(
+        use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
+    )
+
+    policy = TFPolicy(
+        0,
+        behavior_spec,
+        TrainerSettings(network_settings=NetworkSettings(normalize=True)),
+        create_tf_graph=False,
+    )
+    policy.create_input_placeholders()
+    policy.initialize()
+
+    policy.set_step(2 ** 31 - 1)
+    assert policy.get_current_step() == 2 ** 31 - 1
+    policy.increment_step(3)
+    assert policy.get_current_step() == 2 ** 31 + 2
+
+
 if __name__ == "__main__":
     pytest.main()

ml-agents/mlagents/trainers/tests/torch/test_policy.py

 
     if rnn:
         assert memories.shape == (1, 1, policy.m_size)
+
+
+def test_step_overflow():
+    policy = create_policy_mock(TrainerSettings())
+    policy.set_step(2 ** 31 - 1)
+    assert policy.get_current_step() == 2 ** 31 - 1  # step = 2147483647
+    policy.increment_step(3)
+    assert policy.get_current_step() == 2 ** 31 + 2  # step = 2147483650

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

     def create_global_steps():
         """Creates TF ops to track and increment global training step."""
         global_step = tf.Variable(
-            0, name="global_step", trainable=False, dtype=tf.int32
+            0, name="global_step", trainable=False, dtype=tf.int64
-            shape=[], dtype=tf.int32, name="steps_to_increment"
+            shape=[], dtype=tf.int64, name="steps_to_increment"
         )
         increment_step = tf.assign(global_step, tf.add(global_step, steps_to_increment))
         return global_step, increment_step, steps_to_increment
             "normalization_steps",
             [],
             trainable=False,
-            dtype=tf.int32,
+            dtype=tf.int64,
             initializer=tf.zeros_initializer(),
         )
         running_mean = tf.get_variable(
         # Based on Welford's algorithm for running mean and standard deviation, for batch updates. Discussion here:
         # https://stackoverflow.com/questions/56402955/whats-the-formula-for-welfords-algorithm-for-variance-std-with-batch-updates
         steps_increment = tf.shape(vector_input)[0]
-        total_new_steps = tf.add(steps, steps_increment)
+        total_new_steps = tf.add(steps, tf.cast(steps_increment, dtype=tf.int64))
 
         # Compute the incremental update and divide by the number of new steps.
         input_to_old_mean = tf.subtract(vector_input, running_mean)

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

         data = tensor.float_val
     if tensor.int_val:
         data = np.array(tensor.int_val, dtype=float)
+    if tensor.int64_val:
+        data = np.array(tensor.int64_val, dtype=float)
     if tensor.bool_val:
         data = np.array(tensor.bool_val, dtype=float)
     return np.array(data).reshape(dims)

ml-agents/mlagents/trainers/torch/networks.py

 class GlobalSteps(nn.Module):
     def __init__(self):
         super().__init__()
-        self.__global_step = nn.Parameter(torch.Tensor([0]), requires_grad=False)
+        self.__global_step = nn.Parameter(
+            torch.Tensor([0]).to(torch.int64), requires_grad=False
+        )
 
     @property
     def current_step(self):