rename create empty to empty action

ml-agents-envs/mlagents_envs/base_env.py

         """
         return len(self.discrete_branches)
 
-    def create_empty(self, n_agents: int) -> np.ndarray:
+    def empty_action(self, n_agents: int) -> np.ndarray:
         """
         Generates a numpy array corresponding to an empty action (all zeros)
         for a number of agents.

ml-agents-envs/mlagents_envs/environment.py

                     n_agents = len(self._env_state[group_name][0])
                 self._env_actions[group_name] = self._env_specs[
                     group_name
-                ].action_spec.create_empty(n_agents)
+                ].action_spec.empty_action(n_agents)
         step_input = self._generate_step_input(self._env_actions)
         with hierarchical_timer("communicator.exchange"):
             outputs = self._communicator.exchange(step_input)
         num_agents = len(self._env_state[behavior_name][0])
         action = action_spec._validate_action(action, num_agents, behavior_name)
         if behavior_name not in self._env_actions:
-            self._env_actions[behavior_name] = action_spec.create_empty(num_agents)
+            self._env_actions[behavior_name] = action_spec.empty_action(num_agents)
         try:
             index = np.where(self._env_state[behavior_name][0].agent_id == agent_id)[0][
                 0

ml-agents-envs/mlagents_envs/tests/test_envs.py

     env.step()
     decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
     n_agents = len(decision_steps)
-    env.set_actions("RealFakeBrain", spec.action_spec.create_empty(n_agents))
+    env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents))
-        env.set_actions("RealFakeBrain", spec.action_spec.create_empty(n_agents - 1))
+        env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents - 1))
-    env.set_actions("RealFakeBrain", spec.action_spec.create_empty(n_agents) - 1)
+    env.set_actions("RealFakeBrain", spec.action_spec.empty_action(n_agents) - 1)
     env.step()
 
     env.close()

ml-agents-envs/mlagents_envs/tests/test_steps.py

     assert specs.discrete_branches == ()
     assert specs.discrete_size == 0
     assert specs.continuous_size == 3
-    assert specs.create_empty(5).shape == (5, 3)
-    assert specs.create_empty(5).dtype == np.float32
+    assert specs.empty_action(5).shape == (5, 3)
+    assert specs.empty_action(5).dtype == np.float32
-    assert specs.create_empty(5).shape == (5, 1)
-    assert specs.create_empty(5).dtype == np.int32
+    assert specs.empty_action(5).shape == (5, 1)
+    assert specs.empty_action(5).dtype == np.int32
 
 
 def test_action_generator():
-    zero_action = specs.create_empty(4)
+    zero_action = specs.empty_action(4)
     assert np.array_equal(zero_action, np.zeros((4, action_len), dtype=np.float32))
     random_action = specs.random_action(4)
     assert random_action.dtype == np.float32
     # Discrete
     action_shape = (10, 20, 30)
     specs = ActionSpec.create_discrete(action_shape)
-    zero_action = specs.create_empty(4)
+    zero_action = specs.empty_action(4)
     assert np.array_equal(zero_action, np.zeros((4, len(action_shape)), dtype=np.int32))
 
     random_action = specs.random_action(4)