More renaming

ml-agents-envs/mlagents_envs/base_env.py

     """
     A NamedTuple containing information about the observation and action
     spaces for a group of Agents under the same behavior.
-    - sensor_specs is a List of SensorSpec NamedTuple containing
+    - observation_specs is a List of ObservationSpec NamedTuple containing
     information about the information of the Agent's observations such as their shapes.
     The order of the SensorSpec is the same as the order of the observations of an
     agent.

ml-agents-envs/mlagents_envs/rpc_utils.py

     :param agent_info: protobuf object.
     :return: BehaviorSpec object.
     """
-    sensor_specs = []
+    observation_specs = []
-        sensor_specs.append(
+        observation_specs.append(
-                SensorType(obs.sensor_type),
+                ObservationType(obs.sensor_type),
             )
         )
     # proto from communicator < v1.3 does not set action spec, use deprecated fields instead
             action_spec_proto.num_continuous_actions,
             tuple(branch for branch in action_spec_proto.discrete_branch_sizes),
         )
-    return BehaviorSpec(sensor_specs, action_spec)
+    return BehaviorSpec(observation_specs, action_spec)
 
 
 class OffsetBytesIO:
     ]
     decision_obs_list: List[np.ndarray] = []
     terminal_obs_list: List[np.ndarray] = []
-    for obs_index, sensor_specs in enumerate(behavior_spec.observation_specs):
-        is_visual = len(sensor_specs.shape) == 3
+    for obs_index, observation_specs in enumerate(behavior_spec.observation_specs):
+        is_visual = len(observation_specs.shape) == 3
-            obs_shape = cast(Tuple[int, int, int], sensor_specs.shape)
+            obs_shape = cast(Tuple[int, int, int], observation_specs.shape)
             decision_obs_list.append(
                 _process_visual_observation(
                     obs_index, obs_shape, decision_agent_info_list
         else:
             decision_obs_list.append(
                 _process_vector_observation(
-                    obs_index, sensor_specs.shape, decision_agent_info_list
+                    obs_index, observation_specs.shape, decision_agent_info_list
-                    obs_index, sensor_specs.shape, terminal_agent_info_list
+                    obs_index, observation_specs.shape, terminal_agent_info_list
                 )
             )
     decision_rewards = np.array(

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

     n_agents = 10
     shapes = [(3,), (4,)]
     behavior_spec = BehaviorSpec(
-        create_observation_specs_with_shapes(shapes), ActionSpec.create_discrete((2, 2, 6))
+        create_observation_specs_with_shapes(shapes),
+        ActionSpec.create_discrete((2, 2, 6)),
     )
     ap_list = generate_list_agent_proto(n_agents, shapes)
     decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)

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

 
 def test_empty_decision_steps():
     specs = BehaviorSpec(
-        sensor_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
+        observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
         action_spec=ActionSpec.create_continuous(3),
     )
     ds = DecisionSteps.empty(specs)
 
 def test_empty_terminal_steps():
     specs = BehaviorSpec(
-        sensor_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
+        observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
         action_spec=ActionSpec.create_continuous(3),
     )
     ts = TerminalSteps.empty(specs)

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

     return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
 
 
-def create_observation_specs_with_shapes(shapes: List[Tuple[int, ...]]) -> List[SensorSpec]:
+def create_observation_specs_with_shapes(
+    shapes: List[Tuple[int, ...]]
+) -> List[SensorSpec]:
     sen_spec: List[SensorSpec] = []
     for shape in shapes:
         dim_prop = (DimensionProperty.UNSPECIFIED,) * len(shape)