diff --git a/smac/env/starcraft2/starcraft2.py b/smac/env/starcraft2/starcraft2.py
index 4dcae78d..66b2ffea 100644
--- a/smac/env/starcraft2/starcraft2.py
+++ b/smac/env/starcraft2/starcraft2.py
@@ -64,7 +64,7 @@ class StarCraft2Env(MultiAgentEnv):
     def __init__(
         self,
         map_name="8m",
-        step_mul=None,
+        step_mul=8,
         move_amount=2,
         difficulty="7",
         game_version=None,
@@ -91,6 +91,7 @@ def __init__(
         replay_prefix="",
         window_size_x=1920,
         window_size_y=1200,
+        heuristic_ai=False,
         debug=False,
     ):
         """
@@ -102,7 +103,7 @@ def __init__(
             The name of the SC2 map to play (default is "8m"). The full list
             can be found by running bin/map_list.
         step_mul : int, optional
-            How many game steps per agent step (default is None). None
+            How many game steps per agent step (default is 8). None
             indicates to use the default map step_mul.
         move_amount : float, optional
             How far away units are ordered to move per step (default is 2).
@@ -179,6 +180,8 @@ def __init__(
             The length of StarCraft II window size (default is 1920).
         window_size_y: int, optional
             The height of StarCraft II window size (default is 1200).
+        heuristic_ai: bool, optional
+            Whether or not to use a non-learning heuristic AI (default False).
         debug: bool, optional
             Log messages about observations, state, actions and rewards for
             debugging purposes (default is False).
@@ -222,6 +225,7 @@ def __init__(
         self.game_version = game_version
         self.continuing_episode = continuing_episode
         self._seed = seed
+        self.heuristic_ai = heuristic_ai
         self.debug = debug
         self.window_size = (window_size_x, window_size_y)
         self.replay_dir = replay_dir
@@ -348,6 +352,9 @@ def reset(self):
 
         self.last_action = np.zeros((self.n_agents, self.n_actions))
 
+        if self.heuristic_ai:
+            self.heuristic_targets = [None] * self.n_agents
+
         try:
             self._obs = self._controller.observe()
             self.init_units()
@@ -389,7 +396,10 @@ def step(self, actions):
             logging.debug("Actions".center(60, "-"))
 
         for a_id, action in enumerate(actions):
-            agent_action = self.get_agent_action(a_id, action)
+            if not self.heuristic_ai:
+                agent_action = self.get_agent_action(a_id, action)
+            else:
+                agent_action = self.get_agent_action_heuristic(a_id, action)
             if agent_action:
                 sc_actions.append(agent_action)
 
@@ -548,6 +558,59 @@ def get_agent_action(self, a_id, action):
         sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd))
         return sc_action
 
+    def get_agent_action_heuristic(self, a_id, action):
+        unit = self.get_unit_by_id(a_id)
+        tag = unit.tag
+
+        target = self.heuristic_targets[a_id]
+        if unit.unit_type == self.medivac_id:
+            if (target is None or self.agents[target].health == 0 or
+                self.agents[target].health == self.agents[target].health_max):
+                min_dist = math.hypot(self.max_distance_x, self.max_distance_y)
+                min_id = -1
+                for al_id, al_unit in self.agents.items():
+                    if al_unit.unit_type == self.medivac_id:
+                        continue
+                    if (al_unit.health != 0 and
+                        al_unit.health != al_unit.health_max):
+                        dist = self.distance(unit.pos.x, unit.pos.y,
+                                             al_unit.pos.x, al_unit.pos.y)
+                        if dist < min_dist:
+                            min_dist = dist
+                            min_id = al_id
+                self.heuristic_targets[a_id] = min_id
+                if min_id == -1:
+                    self.heuristic_targets[a_id] = None
+                    return None
+            action_id = actions['heal']
+            target_tag = self.agents[self.heuristic_targets[a_id]].tag
+        else:
+            if target is None or self.enemies[target].health == 0:
+                min_dist = math.hypot(self.max_distance_x, self.max_distance_y)
+                min_id = -1
+                for e_id, e_unit in self.enemies.items():
+                    if (unit.unit_type == self.marauder_id and
+                        e_unit.unit_type == self.medivac_id):
+                        continue
+                    if e_unit.health > 0:
+                        dist = self.distance(unit.pos.x, unit.pos.y,
+                                             e_unit.pos.x, e_unit.pos.y)
+                        if dist < min_dist:
+                            min_dist = dist
+                            min_id = e_id
+                self.heuristic_targets[a_id] = min_id
+            action_id = actions['attack']
+            target_tag = self.enemies[self.heuristic_targets[a_id]].tag
+
+        cmd = r_pb.ActionRawUnitCommand(
+            ability_id = action_id,
+            target_unit_tag = target_tag,
+            unit_tags = [tag],
+            queue_command = False)
+
+        sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd))
+        return sc_action
+
     def reward_battle(self):
         """Reward function when self.reward_spare==False.
         Returns accumulative hit/shield point damage dealt to the enemy