remove self.action_spec from policy/bc

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

         condition_sigma_on_obs: bool = True,
     ):
         self.behavior_spec = behavior_spec
-        self.action_spec = behavior_spec.action_spec
-        if self.action_spec.continuous_size > 0 and self.action_spec.discrete_size > 0:
+        if (
+            self.behavior_spec.action_spec.continuous_size > 0
+            and self.behavior_spec.action_spec.discrete_size > 0
+        ):
-            list(self.action_spec.discrete_branches)
-            if self.action_spec.is_discrete()
-            else [self.action_spec.size]
+            list(self.behavior_spec.action_spec.discrete_branches)
+            if self.behavior_spec.action_spec.is_discrete()
+            else [self.behavior_spec.action_spec.size]
         )
         self.vec_obs_size = sum(
             shape[0] for shape in behavior_spec.observation_shapes if len(shape) == 1
         )
-        self.use_continuous_act = self.action_spec.is_continuous()
-        self.num_branches = self.action_spec.size
+        self.use_continuous_act = self.behavior_spec.action_spec.is_continuous()
+        self.num_branches = self.behavior_spec.action_spec.size
         self.previous_action_dict: Dict[str, np.array] = {}
         self.memory_dict: Dict[str, np.ndarray] = {}
         self.normalize = trainer_settings.network_settings.normalize

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

             feed_dict[self.vector_in] = vec_vis_obs.vector_observations
         if not self.use_continuous_act:
             mask = np.ones(
-                (len(batched_step_result), sum(self.action_spec.discrete_branches)),
+                (
+                    len(batched_step_result),
+                    sum(self.behavior_spec.action_spec.discrete_branches),
+                ),
                 dtype=np.float32,
             )
             if batched_step_result.action_mask is not None:
             self.mask = tf.cast(self.mask_input, tf.int32)
 
             tf.Variable(
-                int(self.action_spec.is_continuous()),
+                int(self.behavior_spec.action_spec.is_continuous()),
                 name="is_continuous_control",
                 trainable=False,
                 dtype=tf.int32,
             tf.Variable(
                 self.m_size, name="memory_size", trainable=False, dtype=tf.int32
             )
-            if self.action_spec.is_continuous():
+            if self.behavior_spec.action_spec.is_continuous():
                 tf.Variable(
                     self.act_size[0],
                     name="action_output_shape",

ml-agents/mlagents/trainers/torch/components/bc/module.py

             for the pretrainer.
         """
         self.policy = policy
-        self.action_spec = policy.action_spec
         self._anneal_steps = settings.steps
         self.current_lr = policy_learning_rate * settings.strength
 
                 np.ones(
                     (
                         self.n_sequences * self.policy.sequence_length,
-                        sum(self.action_spec.discrete_branches),
+                        sum(self.policy.behavior_spec.action_spec.discrete_branches),
                     ),
                     dtype=np.float32,
                 )