Fix Gym for ActionSpec

gym-unity/gym_unity/envs/__init__.py

         self._previous_decision_step = decision_steps
 
         # Set action spaces
-        if self.group_spec.is_action_discrete():
-            branches = self.group_spec.discrete_action_branches
-            if self.group_spec.action_size == 1:
+        if self.group_spec.action_spec.is_action_discrete():
+            branches = self.group_spec.action_spec.discrete_action_branches
+            if self.group_spec.action_spec.discrete_action_size == 1:
                 self._action_space = spaces.Discrete(branches[0])
             else:
                 if flatten_branched:
                     "The environment has a non-discrete action space. It will "
                     "not be flattened."
                 )
-            high = np.array([1] * self.group_spec.action_shape)
+            high = np.array([1] * self.group_spec.action_spec.continuous_action_size)
             self._action_space = spaces.Box(-high, high, dtype=np.float32)
 
         # Set observations space
             action = self._flattener.lookup_action(action)
 
         spec = self.group_spec
-        action = np.array(action).reshape((1, spec.action_size))
+        action = np.array(action).reshape((1, spec.action_spec.action_size))
         self._env.set_actions(self.name, action)
 
         self._env.step()

gym-unity/gym_unity/tests/test_gym.py

 from gym_unity.envs import UnityToGymWrapper
 from mlagents_envs.base_env import (
     BehaviorSpec,
-    ActionType,
+    ActionSpec,
     DecisionSteps,
     TerminalSteps,
     BehaviorMapping,
     Creates a mock BrainParameters object with parameters.
     """
     # Avoid using mutable object as default param
-    act_type = ActionType.DISCRETE
-        act_type = ActionType.CONTINUOUS
+        action_spec = ActionSpec(vector_action_space_size, ())
+        action_spec = ActionSpec(0, vector_action_space_size)
-    return BehaviorSpec(obs_shapes, act_type, vector_action_space_size)
+    return BehaviorSpec(obs_shapes, action_spec)
 
 
 def create_mock_vector_steps(specs, num_agents=1, number_visual_observations=0):