Plurals

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

         else:
             ac_class = SharedActorCritic
         self.actor_critic = ac_class(
-            observation_spec=self.behavior_spec.observation_specs,
+            observation_specs=self.behavior_spec.observation_specs,
             network_settings=trainer_settings.network_settings,
             action_spec=behavior_spec.action_spec,
             stream_names=reward_signal_names,

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

 
 def make_fake_trajectory(
     length: int,
-    observation_spec: List[ObservationSpec],
+    observation_specs: List[ObservationSpec],
     action_spec: ActionSpec,
     max_step_complete: bool = False,
     memory_size: int = 10,
     action_size = action_spec.discrete_size + action_spec.continuous_size
     for _i in range(length - 1):
         obs = []
-        for obs_spec in observation_spec:
+        for obs_spec in observation_specs:
             obs.append(np.ones(obs_spec.shape, dtype=np.float32))
         reward = 1.0
         done = False
         )
         steps_list.append(experience)
     obs = []
-    for obs_spec in observation_spec:
+    for obs_spec in observation_specs:
         obs.append(np.ones(obs_spec.shape, dtype=np.float32))
     last_experience = AgentExperience(
         obs=obs,

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

     checkpoint_interval = trainer.trainer_settings.checkpoint_interval
     trajectory = mb.make_fake_trajectory(
         length=time_horizon,
-        observation_spec=create_observation_specs_with_shapes([(1,)]),
+        observation_specs=create_observation_specs_with_shapes([(1,)]),
         max_step_complete=True,
         action_spec=ActionSpec.create_discrete((2,)),
     )

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

     time_horizon = 15
     trajectory = make_fake_trajectory(
         length=time_horizon,
-        observation_spec=optimizer.policy.behavior_spec.observation_specs,
+        observation_specs=optimizer.policy.behavior_spec.observation_specs,
         action_spec=DISCRETE_ACTION_SPEC if discrete else CONTINUOUS_ACTION_SPEC,
         max_step_complete=True,
     )

ml-agents/mlagents/trainers/torch/components/reward_providers/curiosity_reward_provider.py

             memory=None,
         )
         self._state_encoder = NetworkBody(
-            specs.observation_spec, state_encoder_settings
+            specs.observation_specs, state_encoder_settings
         )
 
         self._action_flattener = ActionFlattener(self._action_spec)

ml-agents/mlagents/trainers/torch/components/reward_providers/rnd_reward_provider.py

             vis_encode_type=EncoderType.SIMPLE,
             memory=None,
         )
-        self._encoder = NetworkBody(specs.observation_spec, state_encoder_settings)
+        self._encoder = NetworkBody(specs.observation_specs, state_encoder_settings)
 
     def forward(self, mini_batch: AgentBuffer) -> torch.Tensor:
         n_obs = len(self._encoder.processors)

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

 class SharedActorCritic(SimpleActor, ActorCritic):
     def __init__(
         self,
-        observation_spec: List[ObservationSpec],
+        observation_specs: List[ObservationSpec],
         network_settings: NetworkSettings,
         action_spec: ActionSpec,
         stream_names: List[str],
         self.use_lstm = network_settings.memory is not None
         super().__init__(
-            observation_spec,
+            observation_specs,
             network_settings,
             action_spec,
             conditional_sigma,
 class SeparateActorCritic(SimpleActor, ActorCritic):
     def __init__(
         self,
-        observation_spec: List[ObservationSpec],
+        observation_specs: List[ObservationSpec],
         network_settings: NetworkSettings,
         action_spec: ActionSpec,
         stream_names: List[str],
         self.use_lstm = network_settings.memory is not None
         super().__init__(
-            observation_spec,
+            observation_specs,
             network_settings,
             action_spec,
             conditional_sigma,
-        self.critic = ValueNetwork(stream_names, observation_spec, network_settings)
+        self.critic = ValueNetwork(stream_names, observation_specs, network_settings)
 
     @property
     def memory_size(self) -> int: