Fix typos

ml-agents/mlagents/trainers/optimizer/torch_optimizer.py

     def get_trajectory_value_estimates(
         self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool
     ) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
-        n_obs = len(self.policy.behavior_spec.observation_spec)
+        n_obs = len(self.policy.behavior_spec.observation_specs)
         current_obs = ObsUtil.from_buffer(batch, n_obs)
 
         # Convert to tensors

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

         else:
             ac_class = SharedActorCritic
         self.actor_critic = ac_class(
-            observation_spec=self.behavior_spec.observation_spec,
+            observation_spec=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/sac/optimizer_torch.py

         for name in self.reward_signals:
             rewards[name] = ModelUtils.list_to_tensor(batch[f"{name}_rewards"])
 
-        n_obs = len(self.policy.behavior_spec.observation_spec)
+        n_obs = len(self.policy.behavior_spec.observation_specs)
         current_obs = ObsUtil.from_buffer(batch, n_obs)
         # Convert to tensors
         current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]

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

 ) -> AgentBuffer:
     trajectory = make_fake_trajectory(
         length,
-        behavior_spec.observation_spec,
+        behavior_spec.observation_specs,
         action_spec=behavior_spec.action_spec,
         memory_size=memory_size,
     )

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_spec,
+        observation_spec=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/bc/module.py

         Helper function for update_batch.
         """
         np_obs = ObsUtil.from_buffer(
-            mini_batch_demo, len(self.policy.behavior_spec.observation_spec)
+            mini_batch_demo, len(self.policy.behavior_spec.observation_specs)
         )
         # Convert to tensors
         tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]

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

         )
         dummy_vec_obs = [torch.zeros(batch_dim + [vec_obs_size])]
         # create input shape of NCHW
-        # (It's NHWC in self.policy.behavior_spec.observation_spec.shape)
+        # (It's NHWC in self.policy.behavior_spec.observation_specs.shape)
-            for obs_spec in self.policy.behavior_spec.observation_spec
+            for obs_spec in self.policy.behavior_spec.observation_specs
             if len(obs_spec.shape) == 3
         ]
         dummy_masks = torch.ones(