def get_sc2_action(self, gym_action) -> List[FunctionCall]:
# Get coords by unravelling action.
# How unravel works:
# Ref: https://www.quora.com/What-is-a-simple-intuitive-example-for-the-unravel_index-in-Python
# ===== Testing for A2C =====
# is_attack = gym_action > self.move_space
# if is_attack: gym_action -= self.resolution
coords = np.unravel_index(gym_action, self.unravel_shape)
# PySC2 uses different conventions for observations (y,x) and actions (x,y)
# ::-1 reverses the tuple i.e. (1,2) becomes (2,1)
# ===== Testing for A2C =====
# action = FUNCTIONS.Attack_screen("now", coords[::-1]) if is_attack else FUNCTIONS.Move_screen("now", coords[::-1])
action = FUNCTIONS.Attack_screen("now", coords[::-1])
if action.function not in self.available_actions:
# logger.warning("Attempted unavailable action {}.".format(action))
action = FUNCTIONS.select_army("select")
return [action]
def get_sc2_action(self, gym_action) -> List[FunctionCall]:
if FUNCTIONS.Move_screen.id not in self.available_actions:
return [FUNCTIONS.select_army("select")]
# For restricting the action space for A2C, does not break DQN
gym_action %= self.action_space.n
# Find mean coordinates of all currently active player units
player_units_xy = [(unit.x, unit.y)
for unit in self.state["player_units"]]
arr = np.asarray(player_units_xy)
length = arr.shape[0]
x_sum = np.sum(arr[:, 0])
y_sum = np.sum(arr[:, 1])
centroid = (int(x_sum / length), int(y_sum / length))
# 0: Up
# 1: Down
# 2: Left
# 3: Right
# 4: Up + Left
# 5: Up + Right
# 6: Down + Left
# 7: Down + Right
# 8: Up + Attack
# 9: Down + Attack
# 10: Left + Attack
# 11: Right + Attack
# 12: Up + Left + Attack
# 13: Up + Right + Attack
# 14: Down + Left + Attack
# 15: Down + Right + Attack
is_attack = gym_action > self.move_space
if is_attack: gym_action %= self.adjacency
target_xy = list(centroid)
x_max = self.screen_shape[0] - 1
y_max = self.screen_shape[1] - 1
# Determine target position, diff => min(abs(x_diff), abs(y_diff))
if gym_action == 0: target_xy[1] = 0
elif gym_action == 1: target_xy[1] = y_max
elif gym_action == 2: target_xy[0] = 0
elif gym_action == 3: target_xy[0] = x_max
elif gym_action == 4:
diff = min(centroid[0], centroid[1])
target_xy = [target_xy[0] - diff, target_xy[1] - diff]
elif gym_action == 5:
diff = min(x_max - centroid[0], centroid[1])
target_xy = [target_xy[0] + diff, target_xy[1] - diff]
elif gym_action == 6:
diff = min(centroid[0], y_max - centroid[1])
target_xy = [target_xy[0] - diff, target_xy[1] + diff]
elif gym_action == 7:
diff = min(x_max - centroid[0], y_max - centroid[1])
target_xy = [target_xy[0] + diff, target_xy[1] + diff]
# Assign action function
action = FUNCTIONS.Attack_screen(
"now", target_xy) if is_attack else FUNCTIONS.Move_screen(
"now", target_xy)
return [action]
