def act(self, state):
model = state_to_model(state)
own_id = state["you"]
_, _, is_endgame, min_player_ids = voronoi(model, own_id)
if own_id in min_player_ids:
min_player_ids.remove(own_id)
if not is_endgame and len(min_player_ids) > 1:
pos = model.get_agent_by_id(own_id).pos
opponent_pos = model.get_agent_by_id(min_player_ids[0]).pos
distance_to_next_opponent = distance.euclidean(pos, opponent_pos)
state = reduce_state_to_sliding_window(
state,
distance_to_next_opponent,
min_sliding_window_size=self.min_sliding_window_size,
sliding_window_size_offset=self.sliding_window_size_offset)
move = multiprocessing.Value('i', 4)
reached_depth = multiprocessing.Value('i', 0)
p = multiprocessing.Process(
target=self.depth_first_iterative_deepening,
name="DFID",
args=(move, reached_depth, state))
p.start()
p.join(self.time_for_move)
# Force termination
if p.is_alive():
p.terminate()
p.join()
return Action(move.value)
def voronoi_region_sizes(model, max_player, min_player):
voronoi_cells, voronoi_counter, _, _ = voronoi(model,
max_player.unique_id)
max_player_size = voronoi_counter[
max_player.
unique_id] if max_player.unique_id in voronoi_counter.keys() else 0
min_player_size = voronoi_counter[
min_player.
unique_id] if min_player.unique_id in voronoi_counter.keys() else 0
return voronoi_cells, max_player_size, min_player_size
def init_multi_minimax(self, game_state):
game_state["step"] = self.game_step
model = state_to_model(game_state)
own_id = game_state["you"]
_, _, is_endgame, _ = voronoi(model, own_id)
max_player = model.get_agent_by_id(own_id)
min_player_ids = list(
map(lambda a: a.unique_id, model.active_speed_agents))
min_player_ids.remove(own_id)
move_to_make = Action.CHANGE_NOTHING
max_move = float("-inf")
alpha = float("-inf")
actions = self.init_actions()
return model, max_player, min_player_ids, is_endgame, move_to_make, max_move, alpha, actions
def act(self, state):
self.reached_depth = (False, 0)
model = state_to_model(state)
own_id = state["you"]
_, _, is_endgame, min_player_ids = voronoi(model, own_id)
if own_id in min_player_ids:
min_player_ids.remove(own_id)
self.is_endgame = is_endgame
if not is_endgame and len(min_player_ids) > 1:
pos = model.get_agent_by_id(own_id).pos
opponent_pos = model.get_agent_by_id(min_player_ids[0]).pos
distance_to_next_opponent = distance.euclidean(pos, opponent_pos)
state = reduce_state_to_sliding_window(
state,
distance_to_next_opponent,
min_sliding_window_size=self.min_sliding_window_size,
sliding_window_size_offset=self.sliding_window_size_offset)
self.depth_first_iterative_deepening(state)
return Action(self.move_to_make)
def act(self, state):
model = state_to_model(state)
own_id = state["you"]
_, _, is_endgame, min_player_ids = voronoi(model, own_id)
if own_id in min_player_ids:
min_player_ids.remove(own_id)
if not is_endgame:
pos = model.get_agent_by_id(own_id).pos
opponent_pos = model.get_agent_by_id(min_player_ids[0]).pos
distance_to_next_opponent = distance.euclidean(pos, opponent_pos)
state = reduce_state_to_sliding_window(
state,
distance_to_next_opponent,
min_sliding_window_size=self.min_sliding_window_size,
sliding_window_size_offset=self.sliding_window_size_offset)
move = multiprocessing.Value('i', 4)
reached_depth = multiprocessing.Value('i', 0)
p = multiprocessing.Process(
target=self.depth_first_iterative_deepening,
name="DFID",
args=(move, reached_depth, state))
p.start()
send_time = 1
deadline = datetime.datetime.strptime(state["deadline"],
"%Y-%m-%dT%H:%M:%SZ")
response = requests.get(self.server_time_url)
server_time = datetime.datetime.strptime(response.json()["time"],
"%Y-%m-%dT%H:%M:%SZ")
av_time = (deadline - server_time).total_seconds() - send_time
p.join(av_time)
# If thread is active
if p.is_alive():
# Terminate foo
p.terminate()
p.join()
self.game_step += 1
return Action(move.value)