def test_load_random_map(self):
"""Test loading a few random maps."""
all_maps = maps.get_maps()
run_config = run_configs.get()
with run_config.start() as controller:
# Test only a few random maps when run locally to minimize time.
count = 5
map_sample = random.sample(all_maps.items(), min(count, len(all_maps)))
for _, map_class in sorted(map_sample):
m = map_class()
logging.info("Loading map: %s", m.name)
create = sc_pb.RequestCreateGame(local_map=sc_pb.LocalMap(
map_path=m.path, map_data=m.data(run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer, race=sc_pb.Random,
difficulty=sc_pb.VeryEasy)
join = sc_pb.RequestJoinGame(race=sc_pb.Random,
options=sc_pb.InterfaceOptions(raw=True))
controller.create_game(create)
controller.join_game(join)
# Verify it has the right mods and isn't running into licensing issues.
info = controller.game_info()
logging.info("Mods for %s: %s", m.name, info.mod_names)
self.assertIn("Mods/Void.SC2Mod", info.mod_names)
self.assertIn("Mods/VoidMulti.SC2Mod", info.mod_names)
# Verify it can be played without making actions.
for _ in range(3):
controller.step()
controller.observe()
def test_error(self):
with run_configs.get().start() as controller:
with self.assertRaises(remote_controller.RequestError):
controller.create_game(sc_pb.RequestCreateGame()) # Missing map, etc.
with self.assertRaises(protocol.ProtocolError):
controller.join_game(sc_pb.RequestJoinGame()) # No game to join.
def test_list_all_maps(self):
"""Make sure all maps can be read."""
all_maps = maps.get_maps()
run_config = run_configs.get()
for _, map_class in sorted(all_maps.items()):
map_inst = map_class()
logging.info("map: %s", map_inst.name)
self.assertTrue(map_inst.data(run_config), msg="Failed on %s" % map_inst)
def _initialize(self):
"""Initialize play/replay connection."""
run_config = run_configs.get()
self._map_inst = maps.get(self._config.map_name)
self._map_data = self._map_inst.data(run_config)
self._sc2_proc = run_config.start()
self._controller = self._sc2_proc.controller
def _replay_info(replay_path):
"""Query a replay for information."""
if not replay_path.lower().endswith("sc2replay"):
print("Must be a replay.")
return
run_config = run_configs.get()
with run_config.start() as controller:
info = controller.replay_info(run_config.replay_data(replay_path))
print("-" * 60)
print(info)
def test_ping(self):
count = 100
with run_configs.get().start() as controller:
with stopwatch.sw("first"):
controller.ping()
for _ in range(count):
controller.ping()
self.assertEqual(stopwatch.sw["ping"].num, count)
def _replay_index(replay_dir):
"""Output information for a directory of replays."""
run_config = run_configs.get()
replay_dir = run_config.abs_replay_path(replay_dir)
print("Checking: ", replay_dir)
with run_config.start() as controller:
print("-" * 60)
print(",".join((
"filename",
"build",
"map_name",
"game_duration_loops",
"players",
"P1-outcome",
"P1-race",
"P1-apm",
"P2-race",
"P2-apm",
)))
bad_replays = []
for file_path in run_config.replay_paths(replay_dir):
file_name = os.path.basename(file_path)
info = controller.replay_info(run_config.replay_data(file_path))
if info.HasField("error"):
print("failed:", file_name, info.error, info.error_details)
bad_replays.append(file_name)
else:
out = [
file_name,
info.base_build,
info.map_name,
info.game_duration_loops,
len(info.player_info),
sc_pb.Result.Name(info.player_info[0].player_result.result),
sc_pb.Race.Name(info.player_info[0].player_info.race_actual),
info.player_info[0].player_apm,
]
if len(info.player_info) >= 2:
out += [
sc_pb.Race.Name(info.player_info[1].player_info.race_actual),
info.player_info[1].player_apm,
]
print(u",".join(str(s) for s in out))
if bad_replays:
print("Replays with errors:")
print("\n".join(bad_replays))
def main(unused_argv):
"""Dump stats about all the actions that are in use in a set of replays."""
run_config = run_configs.get()
if not gfile.Exists(FLAGS.replays):
sys.exit("{} doesn't exist.".format(FLAGS.replays))
stats_queue = multiprocessing.Queue()
stats_thread = threading.Thread(target=stats_printer, args=(stats_queue,))
stats_thread.start()
try:
# For some reason buffering everything into a JoinableQueue makes the
# program not exit, so save it into a list then slowly fill it into the
# queue in a separate thread. Grab the list synchronously so we know there
# is work in the queue before the SC2 processes actually run, otherwise
# The replay_queue.join below succeeds without doing any work, and exits.
print("Getting replay list:", FLAGS.replays)
replay_list = sorted(run_config.replay_paths(FLAGS.replays))
print(len(replay_list), "replays found.\n")
replay_queue = multiprocessing.JoinableQueue(FLAGS.parallel * 10)
replay_queue_thread = threading.Thread(target=replay_queue_filler,
args=(replay_queue, replay_list))
replay_queue_thread.daemon = True
replay_queue_thread.start()
for i in range(FLAGS.parallel):
p = ReplayProcessor(i, run_config, replay_queue, stats_queue)
p.daemon = True
p.start()
time.sleep(1) # Stagger startups, otherwise they seem to conflict somehow
replay_queue.join() # Wait for the queue to empty.
except KeyboardInterrupt:
print("Caught KeyboardInterrupt, exiting.")
finally:
stats_queue.put(None) # Tell the stats_thread to print and exit.
stats_thread.join()
def __init__(self,
map_name,
screen_size_px=(64, 64),
minimap_size_px=(64, 64),
camera_width_world_units=None,
discount=1.,
visualize=False,
agent_race=None,
bot_race=None,
difficulty=None,
step_mul=None,
save_replay_steps=0,
replay_dir=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None):
"""Create a SC2 Env.
Args:
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
screen_size_px: The size of your screen output in pixels.
minimap_size_px: The size of your minimap output in pixels.
camera_width_world_units: The width of your screen in world units. If your
screen_size_px=(64, 48) and camera_width is 24, then each px
represents 24 / 64 = 0.375 world units in each of x and y. It'll then
represent a camera of size (24, 0.375 * 48) = (24, 18) world units.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
agent_race: One of P,T,Z,R default random. This is the race you control.
bot_race: One of P,T,Z,R default random. This is the race controlled by
the built-in bot.
difficulty: One of 1-9,A. How strong should the bot be?
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
save_replay_steps: How many game steps to wait before saving a replay.
Default of 0 means don't save replays.
replay_dir: Directory to save replays to. Required with save_replay_steps.
game_steps_per_episode: Game steps per episode, independent of the
step_mul. 0 means no limit. None means use the map default.
score_index: -1 means use the win/loss reward, >=0 is the index into the
score_cumulative with 0 being the curriculum score. None means use
the map default.
score_multiplier: How much to multiply the score by. Useful for negating.
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
"""
# Make the destructor happy.
self._renderer_human = None
self._controller = None
self._sc2_proc = None
if save_replay_steps and not replay_dir:
raise ValueError("Missing replay_dir")
if agent_race and agent_race not in races:
raise ValueError("Bad agent_race args")
if bot_race and bot_race not in races:
raise ValueError("Bad bot_race args")
if difficulty and str(difficulty) not in difficulties:
raise ValueError("Bad difficulty")
self._map = maps.get(map_name)
self._discount = discount
self._step_mul = step_mul or self._map.step_mul
self._save_replay_steps = save_replay_steps
self._replay_dir = replay_dir
self._total_steps = 0
if score_index is None:
self._score_index = self._map.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = self._map.score_multiplier
else:
self._score_multiplier = score_multiplier
self._last_score = None
self._episode_length = (game_steps_per_episode
or self._map.game_steps_per_episode)
self._episode_steps = 0
self._run_config = run_configs.get()
self._sc2_proc = self._run_config.start()
self._controller = self._sc2_proc.controller
screen_size_px = point.Point(*screen_size_px)
minimap_size_px = point.Point(*minimap_size_px)
interface = sc_pb.InterfaceOptions(
raw=visualize,
score=True,
feature_layer=sc_pb.SpatialCameraSetup(
width=camera_width_world_units or 24))
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
create = sc_pb.RequestCreateGame(local_map=sc_pb.LocalMap(
map_path=self._map.path, map_data=self._map.data(
self._run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer,
race=races[bot_race or "R"],
difficulty=difficulties[difficulty or "1"])
join = sc_pb.RequestJoinGame(race=races[agent_race or "R"],
options=interface)
self._controller.create_game(create)
self._controller.join_game(join)
game_info = self._controller.game_info()
static_data = self._controller.data()
self._features = features.Features(game_info)
if visualize:
self._renderer_human = renderer_human.RendererHuman()
self._renderer_human.init(game_info, static_data)
self._episode_count = 0
self._obs = None
self._state = environment.StepType.LAST # Want to jump to `reset`.
logging.info("Environment is ready.")
def _extract_info(replay_name, player_id):
logging.info('enter _extract_info')
logging.info('replay_name: {}, player_id: {}'.format(
replay_name, player_id))
# set the correct sc2 bin path according to the game_version
game_version = FLAGS.game_version
if game_version != '4.7.1' or 'SC2PATH' in os.environ:
# os.environ['SC2PATH'] = '/root/{}'.format(game_version)
os.environ['SC2PATH'] = path.join(FLAGS.sc2mv_bin_root,
game_version)
# reset env/process replay from the beginning
run_config = run_configs.get()
replay_path = path.join(FLAGS.replay_dir, '%s.SC2Replay' % replay_name)
replay_data = run_config.replay_data(replay_path)
# read once
with run_config.start(version=FLAGS.game_version) as controller:
replay_info = controller.replay_info(replay_data)
assert player_id in set(p.player_info.player_id
for p in replay_info.player_info)
player_info = get_player_info_by_player_id(replay_info, player_id)
# collect all infos before starting the replay
map_name = str(replay_info.map_name)
race_actual = _PB_RACE_TO_STR[player_info.player_info.race_actual]
race_requested = _PB_RACE_TO_STR[
player_info.player_info.race_requested]
result = _PB_RESULT_TO_STR[player_info.player_result.result]
mmr = player_info.player_mmr
apm = player_info.player_apm
game_duration_loops = replay_info.game_duration_loops
game_version = replay_info.game_version
map_data = None
if replay_info.local_map_path:
map_data = run_config.map_data(replay_info.local_map_path)
print('using local_map_path {}'.format(
replay_info.local_map_path))
# collect all infos after starting the replay
controller.start_replay(
sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=map_data,
options=get_dft_sc2_interface(),
observed_player_id=player_id,
disable_fog=False))
game_info = controller.game_info()
start_pos = game_info.start_raw.start_locations[0]
# and deem as success
status = 'success'
logging.info('leave _extract_info')
return MyResult(replay_name=replay_name,
player_id=player_id,
map_name=map_name,
race_actual=race_actual,
race_requested=race_requested,
result=result,
mmr=mmr,
apm=apm,
game_duration_loops=game_duration_loops,
game_version=game_version,
start_pos_x=start_pos.x,
start_pos_y=start_pos.y,
status=status)
def __init__(
self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
players=None,
agent_names=None,
agent_race=None, # deprecated
bot_race=None, # deprecated
difficulty=None, # deprecated
screen_size_px=None, # deprecated
minimap_size_px=None, # deprecated
agent_interface_format=None,
discount=1.,
visualize=False,
step_mul=None,
realtime=False,
save_replay_episodes=0,
replay_dir=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None,
random_seed=None,
disable_fog=False,
crop_to_playable_area=False,
show_cloaked=False,
show_burrowed_shadows=False,
show_placeholders=False,
raw_affects_selection=True,
update_game_info=False,
use_pysc2_feature=True,
version=None):
"""Create a SC2 Env.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
players: A list of Agent and Bot instances that specify who will play.
agent_race: Deprecated. Use players instead.
bot_race: Deprecated. Use players instead.
difficulty: Deprecated. Use players instead.
screen_size_px: Deprecated. Use agent_interface_formats instead.
minimap_size_px: Deprecated. Use agent_interface_formats instead.
agent_interface_format: A sequence containing one AgentInterfaceFormat
per agent, matching the order of agents specified in the players list.
Or a single AgentInterfaceFormat to be used for all agents.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
save_replay_episodes: Save a replay after this many episodes. Default of 0
means don't save replays.
replay_dir: Directory to save replays. Required with save_replay_episodes.
game_steps_per_episode: Game steps per episode, independent of the
step_mul. 0 means no limit. None means use the map default.
score_index: -1 means use the win/loss reward, >=0 is the index into the
score_cumulative with 0 being the curriculum score. None means use
the map default.
score_multiplier: How much to multiply the score by. Useful for negating.
random_seed: Random number seed to use when initializing the game. This
lets you run repeatable games/tests.
disable_fog: Whether to disable fog of war.
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
ValueError: if too many players are requested for a map.
ValueError: if the resolutions aren't specified correctly.
DeprecationWarning: if screen_size_px or minimap_size_px are sent.
DeprecationWarning: if agent_race, bot_race or difficulty are sent.
"""
if _only_use_kwargs:
raise ValueError(
"All arguments must be passed as keyword arguments.")
if screen_size_px or minimap_size_px:
raise DeprecationWarning(
"screen_size_px and minimap_size_px are deprecated. Use the feature "
"or rgb variants instead. Make sure to check your observations too "
"since they also switched from screen/minimap to feature and rgb "
"variants.")
if agent_race or bot_race or difficulty:
raise DeprecationWarning(
"Explicit agent and bot races are deprecated. Pass an array of "
"sc2_env.Bot and sc2_env.Agent instances instead.")
map_inst = maps.get(map_name)
self._map_name = map_name
if not players:
players = list()
players.append(Agent(Race.random))
if not map_inst.players or map_inst.players >= 2:
players.append(Bot(Race.random, Difficulty.very_easy))
for p in players:
if not isinstance(p, (Agent, Bot)):
raise ValueError(
"Expected players to be of type Agent or Bot. Got: %s." %
p)
num_players = len(players)
self._num_agents = sum(1 for p in players if isinstance(p, Agent))
self._players = players
if agent_names is None:
self._agent_names = [None] * self._num_agents
else:
assert len(agent_names) == self._num_agents
self._agent_names = agent_names
if not 1 <= num_players <= 2 or not self._num_agents:
raise ValueError("Only 1 or 2 players with at least one agent is "
"supported at the moment.")
if save_replay_episodes and not replay_dir:
raise ValueError("Missing replay_dir")
if map_inst.players and num_players > map_inst.players:
raise ValueError(
"Map only supports %s players, but trying to join with %s" %
(map_inst.players, num_players))
self._discount = discount
self._step_mul = step_mul or map_inst.step_mul
self._realtime = realtime
self._save_replay_episodes = save_replay_episodes
self._replay_dir = replay_dir
self._random_seed = random_seed
self._disable_fog = disable_fog
self._version = version
self._update_game_info = update_game_info
if score_index is None:
self._score_index = map_inst.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = map_inst.score_multiplier
else:
self._score_multiplier = score_multiplier
self._episode_length = game_steps_per_episode
if self._episode_length is None:
self._episode_length = map_inst.game_steps_per_episode
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
if agent_interface_format is None:
raise ValueError("Please specify agent_interface_format.")
if isinstance(agent_interface_format, AgentInterfaceFormat):
agent_interface_format = [agent_interface_format
] * self._num_agents
if len(agent_interface_format) != self._num_agents:
raise ValueError(
"The number of entries in agent_interface_format should "
"correspond 1-1 with the number of agents.")
self.raw = not use_pysc2_feature
interfaces = []
for i, interface_format in enumerate(agent_interface_format):
interfaces.append(
self._get_interface(interface_format, self.raw,
crop_to_playable_area, show_cloaked,
show_burrowed_shadows, show_placeholders,
raw_affects_selection))
if self._num_agents == 1:
self._launch_sp(map_inst, interfaces[0])
else:
self._launch_mp(map_inst, interfaces)
self._finalize(agent_interface_format, interfaces, visualize)
def __init__(
self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
save_map=True,
host="127.0.0.1",
host_port=None,
lan_port=None,
race=None,
agent_interface_format=None,
discount=1.,
visualize=False,
step_mul=None,
replay_dir=None):
"""Create a SC2 Env that connects to a remote instance of the game.
This assumes that the game is already up and running, and that it only
needs to join the game - and leave once the game has ended. You need some
other script to launch the SC2 process and call RequestCreateGame. Note
that you must call close to leave the game when finished. Not doing so
will lead to issues when attempting to create another game on the same
SC2 process.
This class assumes that the game is multiplayer. LAN ports may be
specified either as a base port (from which the others will be implied),
or as an explicit list.
You must specify an agent_interface_format. See the `AgentInterfaceFormat`
documentation for further detail.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
save_map: Whether to save map data before joining the game.
host: Host where the SC2 process we're connecting to is running.
host_port: The WebSocket port for the SC2 process we're connecting to.
lan_port: Either an explicit sequence of LAN ports corresponding to
[server game port, ...base port, client game port, ...base port],
or an int specifying base port - equivalent to specifying the
sequence [lan_port, lan_port+1, lan_port+2, lan_port+3].
race: Race for this agent.
agent_interface_format: AgentInterfaceFormat object describing the
format of communication between the agent and the environment.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
replay_dir: Directory to save a replay.
Raises:
ValueError: if the race is invalid.
ValueError: if the resolutions aren't specified correctly.
ValueError: if lan_port is a sequence but its length != 4.
"""
if _only_use_kwargs:
raise ValueError(
"All arguments must be passed as keyword arguments.")
if agent_interface_format is None:
raise ValueError("Please specify agent_interface_format.")
if not race:
race = sc2_env.Race.random
map_inst = map_name and maps.get(map_name)
self._map_name = map_name
self._num_agents = 1
self._discount = discount
self._step_mul = step_mul or (map_inst.step_mul if map_inst else 8)
self._save_replay_episodes = 1 if replay_dir else 0
self._replay_dir = replay_dir
self._score_index = -1 # Win/loss only.
self._score_multiplier = 1
self._episode_length = 0 # No limit.
self._discount_zero_after_timeout = False
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
self._in_game = False
interface = self._get_interface(
agent_interface_format=agent_interface_format,
require_raw=visualize)
if isinstance(lan_port, collections.Sequence):
if len(lan_port) != 4:
raise ValueError("lan_port sequence must be of length 4")
ports = lan_port[:]
else:
ports = [lan_port + p
for p in range(4)] # 2 * num players *in the game*.
self._connect_remote(host, host_port, ports, race, map_inst, save_map,
interface)
self._finalize([agent_interface_format], [interface], visualize)
def __init__(
self,
replay_file_path,
agent,
player_id=1,
screen_size_px=(64, 64), # (60, 60)
minimap_size_px=(64, 64), # (60, 60)
discount=1.,
frames_per_game=1):
print("Parsing " + replay_file_path)
self.replay_file_name = replay_file_path.split("/")[-1].split(".")[0]
self.agent = agent
self.discount = discount
self.frames_per_game = frames_per_game
self.run_config = run_configs.get()
self.sc2_proc = self.run_config.start()
self.controller = self.sc2_proc.controller
replay_data = self.run_config.replay_data(self.replay_file_name +
'.SC2Replay')
ping = self.controller.ping()
self.info = self.controller.replay_info(replay_data)
# print(self.info)
if not self._valid_replay(self.info, ping):
self.sc2_proc.close()
# print(self.info)
raise Exception(
"{} is not a valid replay file!".format(self.replay_file_name +
'.SC2Replay'))
# global FILE_OP
# FILE_OP.write(self.replay_file_name + '.SC2Replay')
# self.replay_file_name = self.info.map_name+'_'+self.replay_file_name
# for player_info in self.info.player_info:
# race = sc_common.Race.Name(player_info.player_info.race_actual)
# self.replay_file_name = race + '_' + self.replay_file_name
screen_size_px = point.Point(*screen_size_px)
minimap_size_px = point.Point(*minimap_size_px)
interface = sc_pb.InterfaceOptions(
raw=False,
score=True,
feature_layer=sc_pb.SpatialCameraSetup(width=24))
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
map_data = None
if self.info.local_map_path:
map_data = self.run_config.map_data(self.info.local_map_path)
self._episode_length = self.info.game_duration_loops
self._episode_steps = 0
self.controller.start_replay(
sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=map_data,
options=interface,
observed_player_id=player_id))
self._state = StepType.FIRST
def main(unused_argv):
"""Run SC2 to play a game or a replay."""
stopwatch.sw.enabled = FLAGS.profile or FLAGS.trace
stopwatch.sw.trace = FLAGS.trace
if (FLAGS.map and FLAGS.replay) or (not FLAGS.map and not FLAGS.replay):
sys.exit("Must supply either a map or replay.")
if FLAGS.replay and not FLAGS.replay.lower().endswith("sc2replay"):
sys.exit("Replay must end in .SC2Replay.")
if FLAGS.realtime and FLAGS.replay:
# TODO(tewalds): Support realtime in replays once the game supports it.
sys.exit("realtime isn't possible for replays yet.")
if FLAGS.render and (FLAGS.realtime or FLAGS.full_screen):
sys.exit(
"disable pygame rendering if you want realtime or full_screen.")
if platform.system() == "Linux" and (FLAGS.realtime or FLAGS.full_screen):
sys.exit("realtime and full_screen only make sense on Windows/MacOS.")
if not FLAGS.render and FLAGS.render_sync:
sys.exit("render_sync only makes sense with pygame rendering on.")
run_config = run_configs.get()
interface = sc_pb.InterfaceOptions()
interface.raw = FLAGS.render
interface.score = True
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = FLAGS.feature_screen_size
interface.feature_layer.resolution.y = FLAGS.feature_screen_size
interface.feature_layer.minimap_resolution.x = FLAGS.feature_minimap_size
interface.feature_layer.minimap_resolution.y = FLAGS.feature_minimap_size
if FLAGS.rgb_screen_size and FLAGS.rgb_minimap_size:
if FLAGS.rgb_screen_size < FLAGS.rgb_minimap_size:
sys.exit("Screen size can't be smaller than minimap size.")
interface.render.resolution.x = FLAGS.rgb_screen_size
interface.render.resolution.y = FLAGS.rgb_screen_size
interface.render.minimap_resolution.x = FLAGS.rgb_minimap_size
interface.render.minimap_resolution.y = FLAGS.rgb_minimap_size
max_episode_steps = FLAGS.max_episode_steps
if FLAGS.map:
map_inst = maps.get(FLAGS.map)
if map_inst.game_steps_per_episode:
max_episode_steps = map_inst.game_steps_per_episode
create = sc_pb.RequestCreateGame(
realtime=FLAGS.realtime,
disable_fog=FLAGS.disable_fog,
local_map=sc_pb.LocalMap(map_path=map_inst.path,
map_data=map_inst.data(run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(
type=sc_pb.Computer,
race=sc2_env.Race[FLAGS.bot_race],
difficulty=sc2_env.Difficulty[FLAGS.difficulty])
join = sc_pb.RequestJoinGame(race=sc2_env.Race[FLAGS.user_race],
options=interface)
version = None
else:
replay_data = run_config.replay_data(FLAGS.replay)
start_replay = sc_pb.RequestStartReplay(
replay_data=replay_data,
options=interface,
disable_fog=FLAGS.disable_fog,
observed_player_id=FLAGS.observed_player)
version = get_replay_version(replay_data)
with run_config.start(version=version,
full_screen=FLAGS.full_screen) as controller:
if FLAGS.map:
controller.create_game(create)
controller.join_game(join)
else:
info = controller.replay_info(replay_data)
print(" Replay info ".center(60, "-"))
print(info)
print("-" * 60)
map_path = FLAGS.map_path or info.local_map_path
if map_path:
start_replay.map_data = run_config.map_data(map_path)
controller.start_replay(start_replay)
if FLAGS.render:
renderer = renderer_human.RendererHuman(
fps=FLAGS.fps,
step_mul=FLAGS.step_mul,
render_sync=FLAGS.render_sync,
video=FLAGS.video)
renderer.run(run_config,
controller,
max_game_steps=FLAGS.max_game_steps,
game_steps_per_episode=max_episode_steps,
save_replay=FLAGS.save_replay)
else: # Still step forward so the Mac/Windows renderer works.
try:
while True:
frame_start_time = time.time()
if not FLAGS.realtime:
controller.step(FLAGS.step_mul)
obs = controller.observe()
if obs.player_result:
break
time.sleep(
max(0, frame_start_time + 1 / FLAGS.fps - time.time()))
except KeyboardInterrupt:
pass
print("Score: ", obs.observation.score.score)
print("Result: ", obs.player_result)
if FLAGS.map and FLAGS.save_replay:
replay_save_loc = run_config.save_replay(
controller.save_replay(), "local", FLAGS.map)
print("Replay saved to:", replay_save_loc)
# Save scores so we know how the human player did.
with open(replay_save_loc.replace("SC2Replay", "txt"),
"w") as f:
f.write("{}\n".format(obs.observation.score.score))
if FLAGS.profile:
print(stopwatch.sw)
def __init__(
self, # pylint: disable=invalid-name
_only_use_kwargs=None,
host="127.0.0.1",
config_port=None,
race=None,
agent_interface_format=None,
discount=1.,
visualize=False,
step_mul=None,
replay_dir=None,
replay_prefix=None):
"""Create a SC2 Env that connects to a remote instance of the game.
This assumes that the game is already up and running, and it only needs to
join. You need some other script to launch the process and call
RequestCreateGame. It also assumes that it's a multiplayer game, and that
the ports are consecutive.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
host: Which ip to use. Either ipv4 or ipv6 localhost.
config_port: Where to find the config port.
race: Race for this agent.
agent_interface_format: AgentInterfaceFormat object describing the
format of communication between the agent and the environment.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
replay_dir: Directory to save a replay.
replay_prefix: An optional prefix to use when saving replays.
Raises:
ValueError: if the race is invalid.
ValueError: if the resolutions aren't specified correctly.
ValueError: if the host or port are invalid.
"""
if _only_use_kwargs:
raise ValueError(
"All arguments must be passed as keyword arguments.")
if host not in ("127.0.0.1", "::1"):
raise ValueError("Bad host arguments. Must be a localhost")
if not config_port:
raise ValueError("Must pass a config_port.")
if agent_interface_format is None:
raise ValueError("Please specify agent_interface_format.")
if not race:
race = sc2_env.Race.random
self._num_agents = 1
self._discount = discount
self._step_mul = step_mul or 8
self._save_replay_episodes = 1 if replay_dir else 0
self._replay_dir = replay_dir
self._replay_prefix = replay_prefix
self._score_index = -1 # Win/loss only.
self._score_multiplier = 1
self._episode_length = 0 # No limit.
self._ensure_available_actions = False
self._discount_zero_after_timeout = False
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
interface = self._get_interface(
agent_interface_format=agent_interface_format,
require_raw=visualize)
self._launch_remote(host, config_port, race, interface)
self._finalize([agent_interface_format], [interface], visualize)
def run(self):
try:
self.is_running = True
"""A run loop to have agents and an environment interact."""
total_frames = 0
total_episodes = 0
results = [0, 0, 0]
start_time = time()
print("start_time before training:",
strftime("%Y-%m-%d %H:%M:%S", localtime(start_time)))
while time() - start_time < self.max_time_for_training:
self.opponent, _ = self.player.get_match()
agents = [self.player, self.opponent]
# if self.use_replay_expert_reward:
run_config = run_configs.get(
version=self.replay_version
) # the replays released by blizzard are all 3.16.1 version
with self.create_env(self.player, self.opponent) as env:
# set the obs and action spec
observation_spec = env.observation_spec()
action_spec = env.action_spec()
for agent, obs_spec, act_spec in zip(
agents, observation_spec, action_spec):
agent.setup(obs_spec, act_spec)
self.teacher.setup(self.player.agent.obs_spec,
self.player.agent.action_spec)
print('player:', self.player) if debug else None
print('opponent:', self.opponent) if debug else None
print('teacher:', self.teacher) if debug else None
trajectory = []
start_time = time() # in seconds.
print("start_time before reset:",
strftime("%Y-%m-%d %H:%M:%S", localtime(start_time)))
# one opponent match (may include several games) defaultly lasts for no more than 2 hour
while time() - start_time < self.max_time_per_one_opponent:
# Note: the pysc2 environment don't return z
# AlphaStar: home_observation, away_observation, is_final, z = env.reset()
total_episodes += 1
print("total_episodes:", total_episodes)
timesteps = env.reset()
for a in agents:
a.reset()
# check the condition that the replay is over but the game is not
with run_config.start(full_screen=False) as controller:
# here we must use the with ... as ... statement, or it will cause an error
#controller = run_config.start(full_screen=False)
# start replay reward
raw_affects_selection = False
raw_crop_to_playable_area = False
screen_resolution = point.Point(64, 64)
minimap_resolution = point.Point(64, 64)
camera_width = 24
interface = sc_pb.InterfaceOptions(
raw=True,
score=True,
# Omit to disable.
feature_layer=sc_pb.SpatialCameraSetup(
width=camera_width),
# Omit to disable.
render=None,
# By default cloaked units are completely hidden. This shows some details.
show_cloaked=False,
# By default burrowed units are completely hidden. This shows some details for those that produce a shadow.
show_burrowed_shadows=False,
# Return placeholder units (buildings to be constructed), both for raw and feature layers.
show_placeholders=False,
# see below
raw_affects_selection=raw_affects_selection,
# see below
raw_crop_to_playable_area=
raw_crop_to_playable_area)
screen_resolution.assign_to(
interface.feature_layer.resolution)
minimap_resolution.assign_to(
interface.feature_layer.minimap_resolution)
replay_files = os.listdir(self.replay_path)
# random select a replay file from the candidate replays
random.shuffle(replay_files)
replay_path = self.replay_path + replay_files[0]
print('replay_path:', replay_path)
replay_data = run_config.replay_data(replay_path)
replay_info = controller.replay_info(replay_data)
infos = replay_info.player_info
observe_id_list = []
observe_result_list = []
for info in infos:
print('info：', info) if debug else None
player_info = info.player_info
result = info.player_result.result
print('player_info',
player_info) if debug else None
if player_info.race_actual == com_pb.Protoss:
observe_id_list.append(
player_info.player_id)
observe_result_list.append(result)
win_observe_id = 0
for i, result in enumerate(observe_result_list):
if result == sc_pb.Victory:
win_observe_id = observe_id_list[i]
break
start_replay = sc_pb.RequestStartReplay(
replay_data=replay_data,
options=interface,
disable_fog=False, # FLAGS.disable_fog
observed_player_id=
win_observe_id, # FLAGS.observed_player
map_data=None,
realtime=False)
controller.start_replay(start_replay)
feat = F.features_from_game_info(
game_info=controller.game_info(),
raw_resolution=AAIFP.raw_resolution,
hide_specific_actions=AAIFP.
hide_specific_actions,
use_feature_units=True,
use_raw_units=True,
use_unit_counts=True,
use_raw_actions=True,
show_cloaked=True,
show_burrowed_shadows=True,
show_placeholders=True)
replay_obs = None
replay_bo = []
replay_o = controller.observe()
replay_obs = feat.transform_obs(replay_o)
# end replay reward
[home_obs, away_obs] = timesteps
is_final = home_obs.last()
player_memory = self.player.agent.initial_state()
opponent_memory = self.opponent.agent.initial_state(
)
teacher_memory = self.teacher.initial_state()
# initial build order
player_bo = []
episode_frames = 0
# default outcome is 0 (means draw)
outcome = 0
# in one episode (game)
#
start_episode_time = time() # in seconds.
print(
"start_episode_time before is_final:",
strftime("%Y-%m-%d %H:%M:%S",
localtime(start_episode_time)))
while not is_final:
total_frames += 1
episode_frames += 1
state = self.player.agent.agent_nn.preprocess_state_all(
home_obs.observation,
build_order=player_bo)
state_op = self.player.agent.agent_nn.preprocess_state_all(
away_obs.observation)
# baseline_state = self.player.agent.agent_nn.get_scalar_list(home_obs.observation, build_order=player_bo)
# baseline_state_op = self.player.agent.agent_nn.get_scalar_list(away_obs.observation)
baseline_state = self.player.agent.agent_nn.get_baseline_state_from_multi_source_state(
state)
baseline_state_op = self.player.agent.agent_nn.get_baseline_state_from_multi_source_state(
state_op)
player_step = self.player.agent.step_from_state(
state, player_memory)
player_function_call, player_action, player_logits, player_new_memory = player_step
print("player_function_call:",
player_function_call) if debug else None
opponent_step = self.opponent.agent.step_from_state(
state_op, opponent_memory)
opponent_function_call, opponent_action, opponent_logits, opponent_new_memory = opponent_step
# Q: how to do it ?
# teacher_logits = self.teacher(home_obs, player_action, teacher_memory)
# may change implemention of teacher_logits
teacher_step = self.teacher.step_from_state(
state, teacher_memory)
teacher_function_call, teacher_action, teacher_logits, teacher_new_memory = teacher_step
print("teacher_function_call:",
teacher_function_call) if debug else None
env_actions = [
player_function_call,
opponent_function_call
]
player_action_spec = action_spec[0]
action_masks = U.get_mask(
player_action, player_action_spec)
z = None
timesteps = env.step(env_actions)
[home_next_obs, away_next_obs] = timesteps
# print the observation of the agent
# print("home_obs.observation:", home_obs.observation)
reward = home_next_obs.reward
print("reward: ", reward) if debug else None
is_final = home_next_obs.last()
# calculate the build order
player_bo = L.calculate_build_order(
player_bo, home_obs.observation,
home_next_obs.observation)
print("player build order:",
player_bo) if debug else None
# calculate the unit counts of bag
player_ucb = L.calculate_unit_counts_bow(
home_obs.observation).reshape(
-1).numpy().tolist()
print("player unit count of bow:",
sum(player_ucb)) if debug else None
# start replay_reward
# note the controller should step the same steps as with the rl actor (keep the time as the same)
controller.step(STEP_MUL)
replay_next_o = controller.observe()
replay_next_obs = feat.transform_obs(
replay_next_o)
# calculate the build order for replay
replay_bo = L.calculate_build_order(
replay_bo, replay_obs, replay_next_obs)
print("replay build order:",
player_bo) if debug else None
# calculate the unit counts of bag for replay
replay_ucb = L.calculate_unit_counts_bow(
replay_obs).reshape(-1).numpy().tolist()
print("replay unit count of bow:",
sum(replay_ucb)) if debug else None
# end replay_reward
game_loop = home_obs.observation.game_loop[0]
print("game_loop",
game_loop) if debug else None
# note, original AlphaStar pseudo-code has some mistakes, we modified
# them here
traj_step = Trajectory(
state=state,
baseline_state=baseline_state,
baseline_state_op=baseline_state_op,
memory=player_memory,
z=z,
masks=action_masks,
action=player_action,
behavior_logits=player_logits,
teacher_logits=teacher_logits,
is_final=is_final,
reward=reward,
build_order=player_bo,
z_build_order=
replay_bo, # we change it to the sampled build order
unit_counts=player_ucb,
z_unit_counts=
replay_ucb, # we change it to the sampled unit counts
game_loop=game_loop,
)
trajectory.append(traj_step)
player_memory = tuple(
h.detach() for h in player_new_memory)
opponent_memory = tuple(
h.detach() for h in opponent_new_memory)
teacher_memory = tuple(
h.detach() for h in teacher_new_memory)
home_obs = home_next_obs
away_obs = away_next_obs
# for replay reward
replay_obs = replay_next_obs
replay_o = replay_next_o
if is_final:
outcome = reward
print("outcome: ",
outcome) if debug else None
results[outcome + 1] += 1
if len(trajectory) >= AHP.sequence_length:
trajectories = U.stack_namedtuple(
trajectory)
if self.player.learner is not None:
if self.player.learner.is_running:
print("Learner send_trajectory!")
self.player.learner.send_trajectory(
trajectories)
trajectory = []
else:
print("Learner stops!")
print("Actor also stops!")
return
# use max_frames to end the loop
# whether to stop the run
if self.max_frames and total_frames >= self.max_frames:
print("Beyond the max_frames, return!")
return
# use max_frames_per_episode to end the episode
if self.max_frames_per_episode and episode_frames >= self.max_frames_per_episode:
print(
"Beyond the max_frames_per_episode, break!"
)
break
# end of replay
if replay_o.player_result:
print(replay_o.player_result)
break
self.coordinator.send_outcome(
self.player, self.opponent, outcome)
# use max_frames_per_episode to end the episode
if self.max_episodes and total_episodes >= self.max_episodes:
print("Beyond the max_episodes, return!")
print("results: ", results) if debug else None
print("win rate: ", results[2] /
(1e-8 + sum(results))) if debug else None
return
# close the replays
except Exception as e:
print(
"ActorLoop.run() Exception cause return, Detials of the Exception:",
e)
print(traceback.format_exc())
finally:
self.is_running = False
def __init__(self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
host="127.0.0.1",
host_port=None,
lan_port=None,
race=None,
feature_screen_size=None,
feature_screen_width=None,
feature_screen_height=None,
feature_minimap_size=None,
feature_minimap_width=None,
feature_minimap_height=None,
rgb_screen_size=None,
rgb_screen_width=None,
rgb_screen_height=None,
rgb_minimap_size=None,
rgb_minimap_width=None,
rgb_minimap_height=None,
action_space=None,
camera_width_world_units=None,
discount=1.,
visualize=False,
step_mul=None,
replay_dir=None,
use_feature_units=False):
"""Create a SC2 Env that connects to a remote instance of the game.
This assumes that the game is already up and running, and it only needs to
join. You need some other script to launch the process and call
RequestCreateGame. It also assumes that it's a multiplayer game, and that
the ports are consecutive.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
host: Host where the server is running.
host_port: The port for the host.
lan_port: Where to connect to the other SC2 instance.
race: Race for this agent.
feature_screen_size: Sets feature_screen_width and feature_screen_width.
feature_screen_width: The width of the feature layer screen observation.
feature_screen_height: The height of the feature layer screen observation.
feature_minimap_size: Sets feature_minimap_width and
feature_minimap_height.
feature_minimap_width: The width of the feature layer minimap observation.
feature_minimap_height: The height of the feature layer minimap
observation.
rgb_screen_size: Sets rgb_screen_width and rgb_screen_height.
rgb_screen_width: The width of the rgb screen observation.
rgb_screen_height: The height of the rgb screen observation.
rgb_minimap_size: Sets rgb_minimap_width and rgb_minimap_height.
rgb_minimap_width: The width of the rgb minimap observation.
rgb_minimap_height: The height of the rgb minimap observation.
action_space: If you pass both feature and rgb sizes, then you must also
specify which you want to use for your actions as an ActionSpace enum.
camera_width_world_units: The width of your screen in world units. If your
feature_screen=(64, 48) and camera_width is 24, then each px
represents 24 / 64 = 0.375 world units in each of x and y. It'll then
represent a camera of size (24, 0.375 * 48) = (24, 18) world units.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
replay_dir: Directory to save a replay.
use_feature_units: Whether to include feature unit data in observations.
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
ValueError: if too many players are requested for a map.
ValueError: if the resolutions aren't specified correctly.
"""
if _only_use_kwargs:
raise ValueError("All arguments must be passed as keyword arguments.")
if not race:
race = sc2_env.Race.random
map_inst = map_name and maps.get(map_name)
self._map_name = map_name
self._num_agents = 1
self._discount = discount
self._step_mul = step_mul or (map_inst.step_mul if map_inst else 8)
self._save_replay_episodes = 1 if replay_dir else 0
self._replay_dir = replay_dir
self._score_index = -1 # Win/loss only.
self._score_multiplier = 1
self._episode_length = 0 # No limit.
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
interface = self._get_interface(
feature_screen_size, feature_screen_width, feature_screen_height,
feature_minimap_size, feature_minimap_width, feature_minimap_height,
rgb_screen_size, rgb_screen_width, rgb_screen_height, rgb_minimap_size,
rgb_minimap_width, rgb_minimap_height, action_space,
camera_width_world_units, use_feature_units, visualize)
self._connect_remote(host, host_port, lan_port, race, map_inst, interface)
self._finalize(interface, action_space, use_feature_units, visualize)
def host():
"""Run a host which expects one player to connect remotely."""
run_config = run_configs.get()
map_inst = maps.get(FLAGS.map)
if not FLAGS.rgb_screen_size or not FLAGS.rgb_minimap_size:
logging.info(
"Use --rgb_screen_size and --rgb_minimap_size if you want rgb "
"observations.")
while True:
start_port = portpicker.pick_unused_port()
ports = [start_port + p for p in range(4)] # 2 * num_players
if all(portpicker.is_port_free(p) for p in ports):
break
host_proc = run_config.start(extra_ports=ports,
host=FLAGS.host,
timeout_seconds=300,
window_loc=(50, 50))
client_proc = run_config.start(extra_ports=ports,
host=FLAGS.host,
connect=False,
window_loc=(700, 50))
create = sc_pb.RequestCreateGame(
realtime=FLAGS.realtime,
local_map=sc_pb.LocalMap(map_path=map_inst.path))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Participant)
controller = host_proc.controller
controller.save_map(map_inst.path, map_inst.data(run_config))
controller.create_game(create)
print("-" * 80)
print("Join host: play_vs_agent --map %s --host %s --host_port %s "
"--lan_port %s" %
(FLAGS.map, FLAGS.host, client_proc.port, start_port))
print("-" * 80)
join = sc_pb.RequestJoinGame()
join.shared_port = 0 # unused
join.server_ports.game_port = ports.pop(0)
join.server_ports.base_port = ports.pop(0)
join.client_ports.add(game_port=ports.pop(0), base_port=ports.pop(0))
join.race = sc2_env.Race[FLAGS.user_race]
if FLAGS.render:
join.options.raw = True
join.options.score = True
if FLAGS.feature_screen_size and FLAGS.feature_minimap_size:
fl = join.options.feature_layer
fl.width = 24
fl.resolution.x = FLAGS.feature_screen_size
fl.resolution.y = FLAGS.feature_screen_size
fl.minimap_resolution.x = FLAGS.feature_minimap_size
fl.minimap_resolution.y = FLAGS.feature_minimap_size
if FLAGS.rgb_screen_size and FLAGS.rgb_minimap_size:
join.options.render.resolution.x = FLAGS.rgb_screen_size
join.options.render.resolution.y = FLAGS.rgb_screen_size
join.options.render.minimap_resolution.x = FLAGS.rgb_minimap_size
join.options.render.minimap_resolution.y = FLAGS.rgb_minimap_size
controller.join_game(join)
if FLAGS.render:
renderer = renderer_human.RendererHuman(fps=FLAGS.fps,
render_feature_grid=False)
renderer.run(run_configs.get(), controller, max_episodes=1)
else: # Still step forward so the Mac/Windows renderer works.
try:
while True:
frame_start_time = time.time()
if not FLAGS.realtime:
controller.step()
obs = controller.observe()
if obs.player_result:
break
time.sleep(
max(0, frame_start_time - time.time() + 1 / FLAGS.fps))
except KeyboardInterrupt:
pass
for p in [host_proc, client_proc]:
p.close()
def _replay_index(replay_dir):
"""Output information for a directory of replays."""
run_config = run_configs.get()
replay_dir = run_config.abs_replay_path(replay_dir)
print("Checking:", replay_dir)
replay_paths = list(run_config.replay_paths(replay_dir))
print("Found %s replays" % len(replay_paths))
if not replay_paths:
return
data = run_config.replay_data(replay_paths[0])
ver = replay.get_replay_version(data)
FLAGS.set_default("sc2_version", ver.game_version)
run_config = run_configs.get() # In case the version changed.
print("Launching version:", run_config.version.game_version)
with run_config.start(want_rgb=False) as controller:
print("-" * 60)
print(",".join((
"filename",
"version",
"map_name",
"game_duration_loops",
"players",
"P1-outcome",
"P1-race",
"P1-apm",
"P2-race",
"P2-apm",
)))
try:
bad_replays = []
for file_path in replay_paths:
file_name = os.path.basename(file_path)
data = run_config.replay_data(file_path)
try:
info = controller.replay_info(data)
except remote_controller.RequestError as e:
bad_replays.append("%s: %s" % (file_name, e))
continue
if info.HasField("error"):
print("failed:", file_name, info.error, info.error_details)
bad_replays.append(file_name)
else:
out = [
file_name,
info.game_version,
info.map_name,
info.game_duration_loops,
len(info.player_info),
sc_pb.Result.Name(
info.player_info[0].player_result.result),
sc_common.Race.Name(
info.player_info[0].player_info.race_actual),
info.player_info[0].player_apm,
]
if len(info.player_info) >= 2:
out += [
sc_common.Race.Name(
info.player_info[1].player_info.race_actual),
info.player_info[1].player_apm,
]
print(u",".join(str(s) for s in out))
except KeyboardInterrupt:
pass
finally:
if bad_replays:
print("\n")
print("Replays with errors:")
print("\n".join(bad_replays))
def __init__(self,
map_name,
screen_size_px=(64, 64),
minimap_size_px=(64, 64),
camera_width_world_units=None,
discount=1.,
visualize=False,
agent_race=None,
bot_race=None,
difficulty=None,
step_mul=None,
save_replay_steps=0,
replay_dir=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None):
"""Create a SC2 Env.
Args:
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
screen_size_px: The size of your screen output in pixels.
minimap_size_px: The size of your minimap output in pixels.
camera_width_world_units: The width of your screen in world units. If your
screen_size_px=(64, 48) and camera_width is 24, then each px
represents 24 / 64 = 0.375 world units in each of x and y. It'll then
represent a camera of size (24, 0.375 * 48) = (24, 18) world units.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
agent_race: One of P,T,Z,R default random. This is the race you control.
bot_race: One of P,T,Z,R default random. This is the race controlled by
the built-in bot.
difficulty: One of 1-9,A. How strong should the bot be?
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
save_replay_steps: How many game steps to wait before saving a replay.
Default of 0 means don't save replays.
replay_dir: Directory to save replays to. Required with save_replay_steps.
game_steps_per_episode: Game steps per episode, independent of the
step_mul. 0 means no limit. None means use the map default.
score_index: -1 means use the win/loss reward, >=0 is the index into the
score_cumulative with 0 being the curriculum score. None means use
the map default.
score_multiplier: How much to multiply the score by. Useful for negating.
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
"""
# Make the destructor happy.
self._renderer_human = None
self._controller = None
self._sc2_proc = None
if save_replay_steps and not replay_dir:
raise ValueError("Missing replay_dir")
if agent_race and agent_race not in races:
raise ValueError("Bad agent_race args")
if bot_race and bot_race not in races:
raise ValueError("Bad bot_race args")
if difficulty and str(difficulty) not in difficulties:
raise ValueError("Bad difficulty")
self._map = maps.get(map_name)
self._discount = discount
self._step_mul = step_mul or self._map.step_mul
self._save_replay_steps = save_replay_steps
self._replay_dir = replay_dir
self._total_steps = 0
if score_index is None:
self._score_index = self._map.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = self._map.score_multiplier
else:
self._score_multiplier = score_multiplier
self._last_score = None
self._episode_length = (game_steps_per_episode or
self._map.game_steps_per_episode)
self._episode_steps = 0
self._run_config = run_configs.get()
self._sc2_proc = self._run_config.start()
self._controller = self._sc2_proc.controller
screen_size_px = point.Point(*screen_size_px)
minimap_size_px = point.Point(*minimap_size_px)
interface = sc_pb.InterfaceOptions(
raw=visualize, score=True,
feature_layer=sc_pb.SpatialCameraSetup(
width=camera_width_world_units or 24))
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
create = sc_pb.RequestCreateGame(local_map=sc_pb.LocalMap(
map_path=self._map.path, map_data=self._map.data(self._run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer,
race=races[bot_race or "R"],
difficulty=difficulties[difficulty or "1"])
join = sc_pb.RequestJoinGame(race=races[agent_race or "R"],
options=interface)
self._controller.create_game(create)
self._controller.join_game(join)
game_info = self._controller.game_info()
static_data = self._controller.data()
self._features = features.Features(game_info)
if visualize:
self._renderer_human = renderer_human.RendererHuman()
self._renderer_human.init(game_info, static_data)
self._episode_count = 0
self._obs = None
self._state = environment.StepType.LAST # Want to jump to `reset`.
logging.info("Environment is ready.")
def get_random_trajectory(self):
function_dict = {}
for _FUNCTION in actions._FUNCTIONS:
#print(_FUNCTION)
function_dict[_FUNCTION.ability_id] = _FUNCTION.name
race_list = ['Terran', 'Zerg', 'Protoss']
"""How many agent steps the agent has been trained for."""
run_config = run_configs.get()
sc2_proc = run_config.start()
controller = sc2_proc.controller
#print ("source: {}".format(source))
#root_path = '/media/kimbring2/Steam/StarCraftII/Replays/4.8.2.71663-20190123_035823-1'
root_path = self.source
file_list = glob.glob(root_path + '*.*')
#print ("file_list: {}".format(file_list))
for i in range(0, 500):
#print("i: " + str(i))
replay_file_path = random.choice(file_list)
#print ("replay_file_path: {}".format(replay_file_path))
#replay_file_path = root_path + '0a0f62052fe4311368910ad38c662bf979e292b86ad02b49b41a87013e58c432.SC2Replay'
#replay_file_path = root_path + '/0a1b09abc9e98f4e0c3921ae0a427c27e97c2bbdcf34f50df18dc41cea3f3249.SC2Replay'
#replay_file_path_2 = root_path + '/0a01d32e9a98e1596b88bc2cdec7752249b22aca774e3305dae2e93efef34be3.SC2Replay'
#replay_file_path_0 = human_data
#print ("replay_file_path: {}".format(replay_file_path))
try:
replay_data = run_config.replay_data(replay_file_path)
ping = controller.ping()
info = controller.replay_info(replay_data)
print("ping: " + str(ping))
print("replay_info: " + str(info))
player0_race = info.player_info[0].player_info.race_actual
player0_mmr = info.player_info[0].player_mmr
player0_apm = info.player_info[0].player_apm
player0_result = info.player_info[0].player_result.result
print("player0_race: " + str(player0_race))
print("player0_mmr: " + str(player0_mmr))
print("player0_apm: " + str(player0_apm))
print("player0_result: " + str(player0_result))
home_race = race_list.index(self.home_race_name) + 1
if (home_race == player0_race):
print("player0_race pass")
else:
print("player0_race fail")
continue
if (player0_mmr >= self.replay_filter):
print("player0_mmr pass ")
else:
print("player0_mmr fail")
continue
player1_race = info.player_info[0].player_info.race_actual
player1_mmr = info.player_info[0].player_mmr
player1_apm = info.player_info[0].player_apm
player1_result = info.player_info[0].player_result.result
print("player1_race: " + str(player1_race))
print("player1_mmr: " + str(player1_mmr))
print("player1_apm: " + str(player1_apm))
print("player1_result: " + str(player1_result))
away_race = race_list.index(self.away_race_name) + 1
if (away_race == player1_race):
print("player1_race pass ")
else:
print("player1_race fail ")
continue
if (player1_mmr >= self.replay_filter):
print("player1_mmr pass ")
else:
print("player1_mmr fail")
continue
screen_size_px = (128, 128)
minimap_size_px = (64, 64)
player_id = 1
discount = 1.
step_mul = 8
screen_size_px = point.Point(*screen_size_px)
minimap_size_px = point.Point(*minimap_size_px)
interface = sc_pb.InterfaceOptions(raw=False, score=True,
feature_layer=sc_pb.SpatialCameraSetup(width=24))
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
map_data = None
if info.local_map_path:
map_data = run_config.map_data(info.local_map_path)
_episode_length = info.game_duration_loops
_episode_steps = 0
controller.start_replay(sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=map_data, options=interface,
observed_player_id=player_id))
_state = StepType.FIRST
if (info.HasField("error") or
info.base_build != ping.base_build or # different game version
info.game_duration_loops < 1000 or
len(info.player_info) != 2):
# Probably corrupt, or just not interesting.
print("error")
continue
feature_screen_size = 128
feature_minimap_size = 64
rgb_screen_size = None
rgb_minimap_size = None
action_space = None
use_feature_units = True
aif = sc2_env.parse_agent_interface_format(
feature_screen=feature_screen_size,
feature_minimap=feature_minimap_size,
rgb_screen=rgb_screen_size,
rgb_minimap=rgb_minimap_size,
action_space=action_space,
use_feature_units=use_feature_units)
_features = features.features_from_game_info(controller.game_info(), agent_interface_format=aif)
build_info = []
build_name = []
replay_step = 0
print("True loop")
while True:
replay_step += 1
#print("replay_step: " + str(replay_step))
controller.step(step_mul)
obs = controller.observe()
#self.home_trajectory.append(obs)
if (len(obs.actions) != 0):
action = (obs.actions)[0]
action_spatial = action.action_feature_layer
unit_command = action_spatial.unit_command
ability_id = unit_command.ability_id
function_name = function_dict[ability_id]
if (function_name != 'build_queue'):
function_name_parse = function_name.split('_')
function_name_first = function_name_parse[0]
#print("function_name_first: " + str(function_name_first))
if (function_name_first == 'Build' or function_name_first == 'Train'):
unit_name = function_name_parse[1]
unit_info = int(units_new.get_unit_type(self.home_race_name, unit_name)[0])
#print("unit_name: " + str(unit_name))
#print("unit_info: " + str(unit_info))
#print("function_name_parse[1]: " + str(function_name_parse[1]))
build_name.append(unit_name)
build_info.append(unit_info)
if obs.player_result: # Episide over.
_state = StepType.LAST
discount = 0
else:
discount = discount
_episode_steps += step_mul
agent_obs = _features.transform_obs(obs)
self.home_trajectory.append(agent_obs)
step = TimeStep(step_type=_state, reward=0,
discount=discount, observation=agent_obs)
score_cumulative = agent_obs['score_cumulative']
score_cumulative_dict = {}
score_cumulative_dict['score'] = score_cumulative.score
score_cumulative_dict['idle_production_time'] = score_cumulative.idle_production_time
score_cumulative_dict['idle_worker_time'] = score_cumulative.idle_worker_time
score_cumulative_dict['total_value_units'] = score_cumulative.total_value_units
score_cumulative_dict['total_value_structures'] = score_cumulative.total_value_structures
score_cumulative_dict['killed_value_units'] = score_cumulative.killed_value_units
score_cumulative_dict['killed_value_structures'] = score_cumulative.killed_value_structures
score_cumulative_dict['collected_minerals'] = score_cumulative.collected_minerals
score_cumulative_dict['collected_vespene'] = score_cumulative.collected_vespene
score_cumulative_dict['collection_rate_minerals'] = score_cumulative.collection_rate_minerals
score_cumulative_dict['collection_rate_vespene'] = score_cumulative.collection_rate_vespene
score_cumulative_dict['spent_minerals'] = score_cumulative.spent_minerals
score_cumulative_dict['spent_vespene'] = score_cumulative.spent_vespene
#print("obs.player_result: " + str(obs.player_result))
if obs.player_result:
break
_state = StepType.MID
self.home_BO = build_info
self.away_BU = score_cumulative_dict
break
except:
continue
def human():
"""Run a host which expects one player to connect remotely."""
run_config = run_configs.get()
map_inst = maps.get(FLAGS.map)
if not FLAGS.rgb_screen_size or not FLAGS.rgb_minimap_size:
logging.info("Use --rgb_screen_size and --rgb_minimap_size if you want rgb "
"observations.")
ports = [FLAGS.config_port + p for p in range(5)] # tcp + 2 * num_players
if not all(portpicker.is_port_free(p) for p in ports):
sys.exit("Need 5 free ports after the config port.")
proc = None
ssh_proc = None
tcp_conn = None
udp_sock = None
try:
proc = run_config.start(extra_ports=ports[1:], timeout_seconds=300,
host=FLAGS.host, window_loc=(50, 50))
tcp_port = ports[0]
settings = {
"remote": FLAGS.remote,
"game_version": proc.version.game_version,
"realtime": FLAGS.realtime,
"map_name": map_inst.name,
"map_path": map_inst.path,
"map_data": map_inst.data(run_config),
"ports": {
"server": {"game": ports[1], "base": ports[2]},
"client": {"game": ports[3], "base": ports[4]},
}
}
create = sc_pb.RequestCreateGame(
realtime=settings["realtime"],
local_map=sc_pb.LocalMap(map_path=settings["map_path"]))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Participant)
controller = proc.controller
controller.save_map(settings["map_path"], settings["map_data"])
controller.create_game(create)
if FLAGS.remote:
ssh_proc = lan_sc2_env.forward_ports(
FLAGS.remote, proc.host, [settings["ports"]["client"]["base"]],
[tcp_port, settings["ports"]["server"]["base"]])
print("-" * 80)
print("Join: play_vs_agent --host %s --config_port %s" % (proc.host,
tcp_port))
print("-" * 80)
tcp_conn = lan_sc2_env.tcp_server(
lan_sc2_env.Addr(proc.host, tcp_port), settings)
if FLAGS.remote:
udp_sock = lan_sc2_env.udp_server(
lan_sc2_env.Addr(proc.host, settings["ports"]["client"]["game"]))
lan_sc2_env.daemon_thread(
lan_sc2_env.tcp_to_udp,
(tcp_conn, udp_sock,
lan_sc2_env.Addr(proc.host, settings["ports"]["server"]["game"])))
lan_sc2_env.daemon_thread(lan_sc2_env.udp_to_tcp, (udp_sock, tcp_conn))
join = sc_pb.RequestJoinGame()
join.shared_port = 0 # unused
join.server_ports.game_port = settings["ports"]["server"]["game"]
join.server_ports.base_port = settings["ports"]["server"]["base"]
join.client_ports.add(game_port=settings["ports"]["client"]["game"],
base_port=settings["ports"]["client"]["base"])
join.race = sc2_env.Race[FLAGS.user_race]
if FLAGS.render:
join.options.raw = True
join.options.score = True
if FLAGS.feature_screen_size and FLAGS.feature_minimap_size:
fl = join.options.feature_layer
fl.width = 24
FLAGS.feature_screen_size.assign_to(fl.resolution)
FLAGS.feature_minimap_size.assign_to(fl.minimap_resolution)
if FLAGS.rgb_screen_size and FLAGS.rgb_minimap_size:
FLAGS.rgb_screen_size.assign_to(join.options.render.resolution)
FLAGS.rgb_minimap_size.assign_to(join.options.render.minimap_resolution)
controller.join_game(join)
if FLAGS.render:
renderer = renderer_human.RendererHuman(
fps=FLAGS.fps, render_feature_grid=False)
renderer.run(run_configs.get(), controller, max_episodes=1)
else: # Still step forward so the Mac/Windows renderer works.
try:
while True:
frame_start_time = time.time()
if not FLAGS.realtime:
controller.step()
obs = controller.observe()
if obs.player_result:
break
time.sleep(max(0, frame_start_time - time.time() + 1 / FLAGS.fps))
except KeyboardInterrupt:
pass
finally:
if tcp_conn:
tcp_conn.close()
if proc:
proc.close()
if udp_sock:
udp_sock.close()
if ssh_proc:
ssh_proc.terminate()
for _ in range(5):
if ssh_proc.poll() is not None:
break
time.sleep(1)
if ssh_proc.poll() is None:
ssh_proc.kill()
ssh_proc.wait()
def test_multi_player(self):
players = 2
run_config = run_configs.get()
parallel = run_parallel.RunParallel()
map_inst = maps.get("Simple64")
screen_size_px = point.Point(64, 64)
minimap_size_px = point.Point(32, 32)
interface = sc_pb.InterfaceOptions()
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
# Reserve a whole bunch of ports for the weird multiplayer implementation.
ports = [portpicker.pick_unused_port() for _ in range(players * 2)]
logging.info("Valid Ports: %s", ports)
# Actually launch the game processes.
print_stage("start")
sc2_procs = [
run_config.start(extra_ports=ports) for _ in range(players)
]
controllers = [p.controller for p in sc2_procs]
try:
# Save the maps so they can access it.
map_path = os.path.basename(map_inst.path)
print_stage("save_map")
parallel.run((c.save_map, map_path, map_inst.data(run_config))
for c in controllers)
# Create the create request.
create = sc_pb.RequestCreateGame(local_map=sc_pb.LocalMap(
map_path=map_path))
for _ in range(players):
create.player_setup.add(type=sc_pb.Participant)
# Create the join request.
join = sc_pb.RequestJoinGame(race=sc_common.Random,
options=interface)
join.shared_port = 0 # unused
join.server_ports.game_port = ports.pop(0)
join.server_ports.base_port = ports.pop(0)
for _ in range(players - 1):
join.client_ports.add(game_port=ports.pop(0),
base_port=ports.pop(0))
# Play a few short games.
for _ in range(2): # 2 episodes
# Create and Join
print_stage("create")
controllers[0].create_game(create)
print_stage("join")
parallel.run((c.join_game, join) for c in controllers)
print_stage("run")
for game_loop in range(1, 10): # steps per episode
# Step the game
parallel.run(c.step for c in controllers)
# Observe
obs = parallel.run(c.observe for c in controllers)
for p_id, o in enumerate(obs):
self.assertEqual(o.observation.game_loop, game_loop)
self.assertEqual(o.observation.player_common.player_id,
p_id + 1)
# Act
actions = [sc_pb.Action() for _ in range(players)]
for action in actions:
pt = (point.Point.unit_rand() *
minimap_size_px).floor()
pt.assign_to(action.action_feature_layer.camera_move.
center_minimap)
parallel.run(
(c.act, a) for c, a in zip(controllers, actions))
# Done this game.
print_stage("leave")
parallel.run(c.leave for c in controllers)
finally:
print_stage("quit")
# Done, shut down. Don't depend on parallel since it might be broken.
for c in controllers:
c.quit()
for p in sc2_procs:
p.close()
def main():
"""Dump stats about all the actions that are in use in a set of replays."""
run_config = run_configs.get()
filter_file = FLAGS.filter_file
if not gfile.Exists(filter_file):
sys.exit("{} doesn't exist.".format(filter_file))
print("Generating replay list using:", filter_file)
# Instantiate empty replay list
replay_list = []
# Import filter JSON
filtered_data = json.load(open(filter_file))
# Identify replay directory from filter
replay_dir = filtered_data['ReplaysDirectory']
# Iterate through replays denoted in filter
for replay in filtered_data['Replays']:
for key in replay:
if key:
# Generate the replay filename
replay_file = os.path.join(replay_dir, key + '.SC2Replay')
# Append replay file path to replay list
replay_list.append(replay_file)
# Ensure the path for the states directory exists
# else create it.
if not os.path.exists(FLAGS.states_dir):
os.makedirs(FLAGS.states_dir)
stats_queue = multiprocessing.Queue()
stats_thread = threading.Thread(target=stats_printer,
args=(stats_queue, FLAGS.parallel, FLAGS.print_time))
stats_thread.start()
try:
# For some reason buffering everything into a JoinableQueue makes the
# program not exit, so save it into a list then slowly fill it into the
# queue in a separate thread. Grab the list synchronously so we know there
# is work in the queue before the SC2 processes actually run, otherwise
# The replay_queue.join below succeeds without doing any work, and exits.
replay_queue = multiprocessing.JoinableQueue(FLAGS.parallel * 10)
replay_queue_thread = threading.Thread(target=replay_queue_filler,
args=(replay_queue, replay_list))
replay_queue_thread.daemon = True
replay_queue_thread.start()
for i in range(FLAGS.parallel):
p = ReplayProcessor(i, run_config, replay_queue, stats_queue,
FLAGS.step_mul, FLAGS.states_dir, FLAGS.winner_only)
p.daemon = True
p.start()
time.sleep(1) # Stagger startups, otherwise they seem to conflict somehow
replay_queue.join() # Wait for the queue to empty.
except KeyboardInterrupt:
print("Caught KeyboardInterrupt, exiting.")
finally:
stats_queue.put(None) # Tell the stats_thread to print and exit.
stats_thread.join()
def main(unused_argv):
interface = sc_pb.InterfaceOptions()
interface.raw = True
interface.score = True
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = 84
interface.feature_layer.resolution.y = 84
interface.feature_layer.minimap_resolution.x = 64
interface.feature_layer.minimap_resolution.y = 64
timeline = []
start = time.time()
run_config = run_configs.get()
proc = run_config.start()
process = psutil.Process(proc.pid)
episode = 0
def add(s):
cpu = process.cpu_times().user
mem = process.memory_info().rss / 2**20 # In Mb
step = Timestep(episode, time.time() - start, cpu, mem, s)
print(step)
timeline.append(step)
if mem > FLAGS.mem_limit:
raise MemoryException("%s Mb mem limit exceeded" % FLAGS.mem_limit)
try:
add("Started process")
controller = proc.controller
map_inst = maps.get("Simple64")
create = sc_pb.RequestCreateGame(
realtime=False,
disable_fog=False,
local_map=sc_pb.LocalMap(map_path=map_inst.path,
map_data=map_inst.data(run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer,
race=sc_common.Random,
difficulty=sc_pb.CheatInsane)
join = sc_pb.RequestJoinGame(race=sc_common.Random, options=interface)
controller.create_game(create)
add("Created game")
controller.join_game(join)
episode += 1
add("Joined game")
for _ in range(FLAGS.episodes):
for i in range(2000):
controller.step(16)
obs = controller.observe()
if obs.player_result:
add("Lost on step %s" % i)
break
if i > 0 and i % 100 == 0:
add("Step %s" % i)
controller.restart()
episode += 1
add("Restarted")
add("Done")
except KeyboardInterrupt:
pass
except (MemoryException, protocol.ConnectionError) as e:
print(e)
finally:
proc.close()
print("Timeline:")
for t in timeline:
print(t)
def __init__(self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
players=None,
agent_race=None, # deprecated
bot_race=None, # deprecated
difficulty=None, # deprecated
screen_size_px=None, # deprecated
minimap_size_px=None, # deprecated
agent_interface_format=None,
discount=1.,
discount_zero_after_timeout=False,
visualize=False,
step_mul=None,
realtime=False,
save_replay_episodes=0,
replay_dir=None,
replay_prefix=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None,
random_seed=None,
disable_fog=False,
ensure_available_actions=True):
"""Create a SC2 Env.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
players: A list of Agent and Bot instances that specify who will play.
agent_race: Deprecated. Use players instead.
bot_race: Deprecated. Use players instead.
difficulty: Deprecated. Use players instead.
screen_size_px: Deprecated. Use agent_interface_formats instead.
minimap_size_px: Deprecated. Use agent_interface_formats instead.
agent_interface_format: A sequence containing one AgentInterfaceFormat
per agent, matching the order of agents specified in the players list.
Or a single AgentInterfaceFormat to be used for all agents.
discount: Returned as part of the observation.
discount_zero_after_timeout: If True, the discount will be zero
after the `game_steps_per_episode` timeout.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
realtime: Whether to use realtime mode. In this mode the game simulation
automatically advances (at 22.4 gameloops per second) rather than
being stepped manually. The number of game loops advanced with each
call to step() won't necessarily match the step_mul specified. The
environment will attempt to honour step_mul, returning observations
with that spacing as closely as possible. Game loops will be skipped
if they cannot be retrieved and processed quickly enough.
save_replay_episodes: Save a replay after this many episodes. Default of 0
means don't save replays.
replay_dir: Directory to save replays. Required with save_replay_episodes.
replay_prefix: An optional prefix to use when saving replays.
game_steps_per_episode: Game steps per episode, independent of the
step_mul. 0 means no limit. None means use the map default.
score_index: -1 means use the win/loss reward, >=0 is the index into the
score_cumulative with 0 being the curriculum score. None means use
the map default.
score_multiplier: How much to multiply the score by. Useful for negating.
random_seed: Random number seed to use when initializing the game. This
lets you run repeatable games/tests.
disable_fog: Whether to disable fog of war.
ensure_available_actions: Whether to throw an exception when an
unavailable action is passed to step().
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
ValueError: if too many players are requested for a map.
ValueError: if the resolutions aren't specified correctly.
DeprecationWarning: if screen_size_px or minimap_size_px are sent.
DeprecationWarning: if agent_race, bot_race or difficulty are sent.
"""
if _only_use_kwargs:
raise ValueError("All arguments must be passed as keyword arguments.")
if screen_size_px or minimap_size_px:
raise DeprecationWarning(
"screen_size_px and minimap_size_px are deprecated. Use the feature "
"or rgb variants instead. Make sure to check your observations too "
"since they also switched from screen/minimap to feature and rgb "
"variants.")
if agent_race or bot_race or difficulty:
raise DeprecationWarning(
"Explicit agent and bot races are deprecated. Pass an array of "
"sc2_env.Bot and sc2_env.Agent instances instead.")
map_inst = maps.get(map_name)
self._map_name = map_name
if not players:
players = list()
players.append(Agent(Race.random))
if not map_inst.players or map_inst.players >= 2:
players.append(Bot(Race.random, Difficulty.very_easy))
for p in players:
if not isinstance(p, (Agent, Bot, Observer)):
raise ValueError(
"Expected players to be of type Agent, Bot, or Observer. Got: %s." % p)
num_players = len(players)
self._num_agents = sum(1 for p in players if isinstance(p, Agent))
self._num_observers = sum(1 for p in players if isinstance(p, Observer))
self._players = players
if save_replay_episodes and not replay_dir:
raise ValueError("Missing replay_dir")
if map_inst.players and (num_players - self._num_observers) > map_inst.players:
raise ValueError(
"Map only supports %s players, but trying to join with %s" % (
map_inst.players, num_players))
self._discount = discount
self._step_mul = step_mul or map_inst.step_mul
self._realtime = realtime
self._last_step_time = None
self._save_replay_episodes = save_replay_episodes
self._replay_dir = replay_dir
self._replay_prefix = replay_prefix
self._random_seed = random_seed
self._disable_fog = disable_fog
self._ensure_available_actions = ensure_available_actions
self._discount_zero_after_timeout = discount_zero_after_timeout
if score_index is None:
self._score_index = map_inst.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = map_inst.score_multiplier
else:
self._score_multiplier = score_multiplier
self._episode_length = game_steps_per_episode
if self._episode_length is None:
self._episode_length = map_inst.game_steps_per_episode
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
if agent_interface_format is None:
raise ValueError("Please specify agent_interface_format.")
if isinstance(agent_interface_format, AgentInterfaceFormat):
agent_interface_format = [agent_interface_format] * (self._num_agents + self._num_observers)
if len(agent_interface_format) != (self._num_agents + self._num_observers):
raise ValueError(
"The number of entries in agent_interface_format should "
"correspond 1-1 with the number of agents plus the number of observers.")
interfaces = []
for i, interface_format in enumerate(agent_interface_format):
require_raw = visualize and (i == 0)
interfaces.append(self._get_interface(interface_format, require_raw))
if self._num_agents == 1:
self._launch_sp(map_inst, interfaces[0])
else:
self._launch_mp(map_inst, interfaces)
self._finalize(agent_interface_format, interfaces, visualize)
def __init__(
self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
host="127.0.0.1",
host_port=None,
lan_port=None,
race=None,
agent_interface_format=None,
discount=1.,
visualize=False,
step_mul=None,
replay_dir=None):
"""Create a SC2 Env that connects to a remote instance of the game.
This assumes that the game is already up and running, and it only needs to
join. You need some other script to launch the process and call
RequestCreateGame. It also assumes that it's a multiplayer game, and that
the ports are consecutive.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
host: Host where the server is running.
host_port: The port for the host.
lan_port: Where to connect to the other SC2 instance.
race: Race for this agent.
agent_interface_format: AgentInterfaceFormat object describing the
format of communication between the agent and the environment.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
replay_dir: Directory to save a replay.
Raises:
ValueError: if the race is invalid.
ValueError: if the resolutions aren't specified correctly.
"""
if _only_use_kwargs:
raise ValueError(
"All arguments must be passed as keyword arguments.")
if agent_interface_format is None:
raise ValueError("Please specify agent_interface_format.")
if not race:
race = sc2_env.Race.random
map_inst = map_name and maps.get(map_name)
self._map_name = map_name
self._num_agents = 1
self._discount = discount
self._step_mul = step_mul or (map_inst.step_mul if map_inst else 8)
self._save_replay_episodes = 1 if replay_dir else 0
self._replay_dir = replay_dir
self._score_index = -1 # Win/loss only.
self._score_multiplier = 1
self._episode_length = 0 # No limit.
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
interface = self._get_interface(
agent_interface_format=agent_interface_format, require_raw=True)
self._connect_remote(host, host_port, lan_port, race, map_inst,
interface)
self._finalize([agent_interface_format], [interface], visualize)
def extract_actions(counter, replays_path, save_path, batch_size, step_mul):
# Check if the replays_path exists
if not os.path.isdir(replays_path):
raise ValueError('The path ' + replays_path + ' does not exist.')
# Make list of the paths to all the replays
cwd = os.getcwd()
replay_paths = []
for replay in os.listdir(replays_path):
replay_path = os.path.join(cwd, replays_path, replay)
if os.path.isfile(replay_path) and replay.lower().endswith(
'.sc2replay'):
replay_paths.append(replay_path)
# Check if the save_path exists. Otherwise we need to create it
if not os.path.isdir(save_path):
os.makedirs(save_path)
# Used for picking up from where an exception was thrown.
replays_range_left_in_batch = (0, 0)
while True:
try:
run_config = run_configs.get()
with run_config.start() as controller:
while True:
# Variables for later
batch_start = 0
batch_end = 0
# Check if we resumed here from an exception
if replays_range_left_in_batch == (0, 0):
# Everything is good
with counter.get_lock():
if counter.value * batch_size > len(replay_paths):
print(
' Reached the end of the replay list. Returning...'
)
return
batch_number = counter.value
counter.value += 1
batch_start = batch_number * batch_size
batch_end = batch_number * batch_size + batch_size
if batch_end > len(replay_paths) - 1:
batch_end = len(replay_paths) - 1
else:
# We resumed here from an exception. Skip the replay that caused an exception.
batch_start = replays_range_left_in_batch[0] + 1
batch_end = replays_range_left_in_batch[1]
print(
' Resuming with batch from a crash. Resuming from '
+ str(batch_start) + ' to ' + str(batch_end))
for index in range(batch_start, batch_end):
print(
'================================================================================ Processing replay #'
+ str(index + 1))
replays_range_left_in_batch = (index, batch_end)
replay_path = replay_paths[index]
replay_data = run_config.replay_data(replay_path)
info = controller.replay_info(replay_data)
interface = sc_pb.InterfaceOptions()
interface.raw = True
interface.score = False
for player in info.player_info:
player_game_data_points = []
start_replay = sc_pb.RequestStartReplay(
replay_data=replay_data,
options=interface,
disable_fog=False,
observed_player_id=player.player_info.player_id
)
controller.start_replay(start_replay)
controller.step()
time_step = 0
resources = []
upgrades = []
in_progress = []
friendly_unit_list = []
enemy_unit_list = []
try:
while True:
time_step += step_mul
controller.step(step_mul)
obs = controller.observe()
for action in obs.actions:
mapped_action = get_macro_action(
action)
if mapped_action is not None:
# print('________________________________________________________________________________')
# print('Time step (step/seconds): ' + str(time_step) + '/' + str(time_step/22.4))
# print('Resources (minerals, vespene, food(cap, used, army, workers), idle_workers, army_count, warp_gates): ' + str(resources[0]) + ', ' + str(resources[1]) + ', (' + str(resources[2]) + ', ' + str(resources[3]) + ', ' + str(resources[4]) + ', ' + str(resources[5]) + '), ' + str(resources[6]) + ', ' + str(resources[7]) + ', ' + str(resources[8]))
# print('In progress: ' + str(in_progress_dic(in_progress)))
# print('Upgrades: ' + str(upgrades_dic(upgrades)))
# print('Friendly buildings: ' + str(buildings_dic(friendly_unit_list)))
# print('Friendly units: ' + str(units_dic(friendly_unit_list)))
# print('Enemy buildings: ' + str(buildings_dic(enemy_unit_list)))
# print('Enemy units: ' + str(units_dic(enemy_unit_list)))
# print('--------------------------------------------------------------------------------')
# action_id = np.argmax(mapped_action)
# print('Action: ' + str(action_id) + ': ' + c.protoss_macro_actions[np.argmax(mapped_action)])
# print('________________________________________________________________________________')
new_data_point = []
new_data_point.append(
time_step) # 1
new_data_point += resources # 9
new_data_point += upgrades # 26
new_data_point += in_progress # 70
new_data_point += friendly_unit_list # 44
new_data_point += enemy_unit_list # 44
new_data_point += mapped_action # 54
player_game_data_points.append(
new_data_point)
resources = get_resources(
obs.observation)
upgrades = get_upgrades(
obs.observation.raw_data.player.
upgrade_ids)
in_progress = get_units_in_progress(
obs.observation.raw_data.units)
friendly_unit_list = get_friendly_unit_list(
obs.observation.raw_data.units)
enemy_unit_list = get_enemy_unit_list(
obs.observation.raw_data.units)
# The game has finished if there is a player_result
if obs.player_result:
print(
' Replay #'
+ str(index + 1) +
' from player ' +
str(player.player_info.player_id) +
'\'s perspective has finished.')
break
except KeyboardInterrupt:
return
# Save the data for this player.
replay_save_path = os.path.join(
cwd, save_path,
replay_path.split('/')[-1].split('.')[0] +
'_' + str(player.player_info.player_id))
save_replay_data(replay_save_path,
player_game_data_points)
print(
' Finished processing game #'
+ str(index + 1) + '.')
# Everything went smothely with this replay batch. Reset the counter and move on.
replays_range_left_in_batch = (0, 0)
except KeyboardInterrupt:
return
except WebSocketTimeoutException:
print(
' Websocket timed out.'
)
except pysc2.lib.protocol.ConnectionError:
print(
' Websocket timed out.'
)
except:
print(
' Something went wrong. Skipping this replay.'
)
def main(unused_argv):
stopwatch.sw.enable()
results = []
try:
for config, interface in configs:
print((" Starting: %s " % config).center(60, "-"))
timeline = []
run_config = run_configs.get()
if FLAGS.replay:
replay_data = run_config.replay_data(FLAGS.replay)
start_replay = sc_pb.RequestStartReplay(
replay_data=replay_data,
options=interface,
disable_fog=False,
observed_player_id=2)
version = replay.get_replay_version(replay_data)
run_config = run_configs.get(
version=version) # Replace the run config.
else:
map_inst = maps.get(FLAGS.map)
create = sc_pb.RequestCreateGame(
realtime=False,
disable_fog=False,
random_seed=1,
local_map=sc_pb.LocalMap(
map_path=map_inst.path,
map_data=map_inst.data(run_config)))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer,
race=sc_common.Terran,
difficulty=sc_pb.VeryEasy)
join = sc_pb.RequestJoinGame(options=interface,
race=sc_common.Protoss)
with run_config.start(
want_rgb=interface.HasField("render")) as controller:
if FLAGS.replay:
info = controller.replay_info(replay_data)
print(" Replay info ".center(60, "-"))
print(info)
print("-" * 60)
if info.local_map_path:
start_replay.map_data = run_config.map_data(
info.local_map_path)
controller.start_replay(start_replay)
else:
controller.create_game(create)
controller.join_game(join)
for _ in range(FLAGS.count):
controller.step(FLAGS.step_mul)
start = time.time()
obs = controller.observe()
timeline.append(time.time() - start)
if obs.player_result:
break
results.append((config, timeline))
except KeyboardInterrupt:
pass
names, values = zip(*results)
print("\n\nTimeline:\n")
print(",".join(names))
for times in zip(*values):
print(",".join("%0.2f" % (t * 1000) for t in times))
print(stopwatch.sw)
def main(unused_argv, version=STANDARD_VERSION):
version = REPLAY_VERSION
replay_files = []
for root, dir, files in os.walk(os.path.join(REPLAY_DIR, version)):
for file in files:
if file.endswith(".SC2Replay"):
replay_files.append(os.path.join(root, file))
for replay_file in replay_files:
print('Going to parse replay "{}".'.format(replay_file),
file=sys.stderr)
REPLAYS_SINGLE = os.path.join(
REPLAYS_PARSED_DIR, version + '_2',
os.path.relpath(replay_file,
os.path.join(REPLAY_DIR, version)).replace(
'.SC2Replay', ''))
LOG_SINGLE = os.path.join(
REPO_DIR, 'log', version + '_2',
os.path.relpath(replay_file,
os.path.join(REPLAY_DIR, version)).replace(
'.SC2Replay', ''))
os.makedirs(REPLAYS_SINGLE, exist_ok=True)
os.makedirs(LOG_SINGLE, exist_ok=True)
run_config = run_configs.get()
PATH = MAP_PATH + '-v' + version + '.SC2Map'
replay_data = run_config.replay_data(replay_file)
with run_config.start(game_version=get_replay_version(replay_data),
full_screen=False) as controller:
print('Starting replay...', file=sys.stderr)
controller.start_replay(
RequestStartReplay(
replay_data=replay_data,
# Overwrite map_data because we know where our map is located
# (so we don't care about the map path stored in the replay).
map_data=run_config.map_data(PATH),
# Observe from Team Minerals so player_relative features work.
observed_player_id=1,
# Controls what type of observations we will receive
options=InterfaceOptions(
# Raw observations include absolute unit owner, position,
# and type we enable them for constructing a baseline.
raw=True,
# Score observations include statistics collected over the
# game, most aren't applicable to this map, so disable them.
score=False,
# Feature layer observations are those described in the
# SC2LE paper. We want to learn on those, so enable them.
feature_layer=SpatialCameraSetup(
width=24,
resolution=Size2DI(x=SCREEN_RESOLUTION,
y=SCREEN_RESOLUTION),
minimap_resolution=Size2DI(
x=MINIMAP_RESOLUTION,
y=MINIMAP_RESOLUTION))),
# Ensure that fog of war won't hinder observations.
disable_fog=True))
info = controller.replay_info(replay_data=replay_data)
time = info.game_duration_seconds
if time > 6000:
os.remove(replay_file)
print(
'removed %s because game time exceeded 1 hour (actual replay duration was %.2f)'
% (replay_file, time))
break
else:
print('File okay, Duration is %.2f' % (time))
round_num = 0
curr_battle = None
last_num_units, curr_num_units = -1, -1
last_num_wins_minerals, curr_num_wins_minerals = -1, -1
last_num_wins_vespene, curr_num_wins_vespene = -1, -1
units = []
obs = controller.observe()
while not obs.player_result:
# We advance 16 steps at a time here because this is the largest
# value which still ensures that we will have at least one
# observation between different phases (units spawn, units
# engage, outcome determined, units remove) when wait time is
# set to 1.0 game seconds in editor.
controller.step(16)
obs = controller.observe()
#save unit and win values from last round
last_num_units = curr_num_units
last_num_wins_minerals = curr_num_wins_minerals
last_num_wins_vespene = curr_num_wins_vespene
#get values from active round
curr_num_units = len(obs.observation.raw_data.units)
curr_num_wins_minerals = \
obs.observation.player_common.minerals
curr_num_wins_vespene = \
obs.observation.player_common.vespene
if curr_num_units != 0 and last_num_units == 0 and curr_battle is None:
broodling = False
units = obs.observation.raw_data.units
for unit in units:
if unit.unit_type == 289:
broodling = True
round_num += 1
print('Parsing Round {}...'.format(round_num),
file=sys.stderr)
assert curr_battle is None
curr_battle = obs.observation
unit_array = obs.observation.raw_data.units
units = []
units.append([])
units.append([])
for unit in unit_array:
if unit.owner == 1:
units[0].append(unit.unit_type)
elif unit.owner == 2:
units[1].append(unit.unit_type)
outcome = None
if (curr_num_wins_minerals > last_num_wins_minerals) and (
curr_num_wins_vespene > last_num_wins_vespene):
units.append([2])
outcome = [0, 0, 1]
elif (curr_num_wins_minerals > last_num_wins_minerals) \
and (last_num_wins_minerals != -1):
units.append([0])
outcome = [1, 0, 0]
elif (curr_num_wins_vespene > last_num_wins_vespene) \
and (last_num_wins_vespene != -1):
units.append([1])
outcome = [0, 1, 0]
if outcome is not None:
if curr_battle is not None:
parsed_replay_path = os.path.join(
REPO_DIR, 'Proc_Data_', version,
os.path.relpath(
replay_file,
os.path.join(REPLAY_DIR, version)).replace(
'.SC2Replay', ''),
os.path.relpath(
replay_file,
os.path.join(REPLAY_DIR, version)).replace(
'.SC2Replay', '') + '_' +
str(round_num))
print('Round %s parsed and saved as: %s' %
(round_num, parsed_replay_path),
file=sys.stderr)
player_relative = curr_battle.feature_layer_data.renders.player_relative
height_map = curr_battle.feature_layer_data.renders.height_map
visibility = curr_battle.feature_layer_data.renders.visibility_map
creep = curr_battle.feature_layer_data.renders.creep
power = curr_battle.feature_layer_data.renders.power
player_id = curr_battle.feature_layer_data.renders.player_id
unit_type = curr_battle.feature_layer_data.renders.unit_type
selected = curr_battle.feature_layer_data.renders.selected
hit_points = curr_battle.feature_layer_data.renders.unit_hit_points
energy = curr_battle.feature_layer_data.renders.unit_energy
shields = curr_battle.feature_layer_data.renders.unit_shields
unit_density = curr_battle.feature_layer_data.renders.unit_density
unit_density_aa = curr_battle.feature_layer_data.renders.unit_density_aa
feature_layers = []
# # computing feature layers in a Nx84x84 Tensor
# # Player Relative
data_pr = np.frombuffer(player_relative.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_pr[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# # height map
data_hm = np.frombuffer(height_map.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_hm[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# # visibility
data_vi = np.frombuffer(visibility.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_vi[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# # creep
data_cr = np.frombuffer(creep.data, dtype=np.int8) \
.reshape((1, -1, 2))
data_processed = data_cr[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# # power
data_pw = np.frombuffer(power.data, dtype=np.int8) \
.reshape((1, -1, 2))
data_processed = data_pw[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# player id
data_id = np.frombuffer(player_id.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_id[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# unit_type
data_ut = np.frombuffer(unit_type.data, dtype=np.int32) \
.reshape((1, 84, 84))
feature_layers.append(data_ut[0])
# # # selected
data_se = np.frombuffer(selected.data, dtype=np.int8) \
.reshape((1, -1, 2))
data_processed = data_se[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# hit points
data_hp = np.frombuffer(hit_points.data, dtype=np.int32) \
.reshape((1, 84, 84))
feature_layers.append(data_hp[0])
# # energy
data_en = np.frombuffer(energy.data, dtype=np.int32) \
.reshape((1, 84, 84))
feature_layers.append(data_en[0])
# # shields
data_sh = np.frombuffer(shields.data, dtype=np.int32) \
.reshape((1, 84, 84))
feature_layers.append(data_sh[0])
# # unit density
data_de = np.frombuffer(unit_density.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_de[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
# # unit density aa
data_da = np.frombuffer(unit_density_aa.data, dtype=np.int32) \
.reshape((1, 42, 42))
data_processed = data_da[0].copy()
data_processed = np.resize(data_processed,
(84, 84))
feature_layers.append(data_processed)
layers = []
for layer in feature_layers:
layer = np.reshape(layer, (84, 84))
layers.append(layer)
layers = np.reshape(layers, (13 * 84, 84))
layers = np.append(layers, outcome)
data = pd.DataFrame(layers)
os.makedirs(parsed_replay_path, exist_ok=True)
data.to_csv(os.path.join(parsed_replay_path,
'Layer.csv'),
header=False,
index=False,
compression='gzip')
units = np.array(units)
df = pd.DataFrame(units)
df.to_csv(
os.path.join(
LOG_SINGLE,
os.path.relpath(
replay_file,
os.path.join(REPLAY_DIR,
version)).replace(
'.SC2Replay',
'_' + str(round_num) +
'.csv')))
units = []
curr_battle = None
# if (error_flag == False):
# replay_parsed_file = os.path.join(
# REPLAYS_PARSED_DIR,
# os.path.relpath(replay_file, REPLAY_DIR).replace(
# '.SC2Replay', '.SC2Replay_parsed'))
# print('Replay completed. Saving parsed replay to "{}".'
# .format(replay_parsed_file), file=sys.stderr)
# os.makedirs(REPLAYS_PARSED_DIR, exist_ok=True)
# with open(replay_parsed_file, 'wb') as file:
# file.write(simulation.SerializeToString())
# os.rename(replay_file, os.path.join(REPO_DIR, "parsed_basic", os.path.relpath(replay_file, REPLAY_DIR)))
os.makedirs(os.path.join(REPO_DIR, 'parsed_basic', version),
exist_ok=True)
os.rename(
replay_file,
os.path.join(
REPO_DIR, "parsed_basic", version,
os.path.relpath(replay_file,
os.path.join(REPLAY_DIR, version))))
print('Done.', file=sys.stderr)
def mnet_v6d6_run_il_loss_test():
mycfg = {
'test': False,
'use_self_fed_heads': False,
'use_value_head': False,
'use_loss_type': 'il',
'il_multi_label_loss': True,
'use_lstm': True,
'lstm_cell_type': 'lstm',
'lstm_layer_norm': True,
'batch_size': 2,
'rollout_len': 1,
'lstm_duration': 1,
'nlstm': 256,
'hs_len': 256 * 2,
'weight_decay': 0.0005,
'arg_scope_type': 'mnet_v6_type_a',
'use_base_mask': True,
'vec_embed_version': 'v2d1',
'last_act_embed_version': 'v2',
'zstat_embed_version': 'v7',
'trans_version': 'v5',
'gather_batch': True,
'use_astar_glu': True,
'use_astar_func_embed': True,
'pos_logits_mode': '3x3up2',
'pos_n_blk': 3,
'pos_n_skip': 2,
}
converter = PB2AllConverter(dict_space=True,
zmaker_version='v5',
zstat_data_src=FLAGS.zstat_data_src,
game_version=FLAGS.game_version,
sort_executors='v1',
delete_dup_action='v2',
input_map_size=(128, 128),
output_map_size=(256, 256))
ob_space, ac_space = converter.space.spaces
# build the net
nc = net_config_cls(ob_space, ac_space, **mycfg)
inputs = net_inputs_placeholders_fun(nc)
out = net_build_fun(inputs, nc, scope='mnet_v6d6_il_loss')
print('Successfully created the net.')
converter.reset(replay_name=FLAGS.replay_name,
player_id=FLAGS.player_id,
mmr=6000,
map_name=FLAGS.map_name)
run_config = run_configs.get()
replay_data = run_config.replay_data(
path.join(FLAGS.replay_dir, FLAGS.replay_name + '.SC2Replay'))
with run_config.start(version=FLAGS.game_version) as controller:
replay_info = controller.replay_info(replay_data)
print(replay_info)
controller.start_replay(
sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=None,
options=get_replay_actor_interface(
FLAGS.map_name),
observed_player_id=FLAGS.player_id,
disable_fog=False))
controller.step()
last_pb = None
last_game_info = None
sess = tf.Session()
tf.global_variables_initializer().run(session=sess)
for _ in range(1000):
pb_obs = controller.observe()
game_info = controller.game_info()
if last_pb is None:
last_pb = pb_obs
last_game_info = game_info
continue
if pb_obs.player_result:
# episode ends, the zstat to this extent is what we need
break
# pb2all: X, A, weight as data
data = converter.convert(pb=(last_pb, last_game_info),
next_pb=(pb_obs, game_info))
if len(data) > 0:
X, A = data[0][0]
feed_dict = {}
for key in X:
feed_dict[inputs.X[key]] = [X[key]] * mycfg['batch_size']
for key in A:
feed_dict[inputs.A[key]] = [A[key]] * mycfg['batch_size']
feed_dict[inputs.S] = (
[np.array([0] * mycfg['hs_len'], dtype=np.float32)] *
mycfg['batch_size'])
feed_dict[inputs.M] = ([np.array(1, dtype=np.bool)] *
mycfg['batch_size'])
loss = sess.run(out.loss.loss_endpoints, feed_dict)
ab = A['A_AB']
avail_actions = np.nonzero(X['MASK_AB'])[0]
avail_selections = np.nonzero(X['MASK_SELECTION'][ab])[0]
selection_units = A['A_SELECT']
print('Avail action num: {}, select {}'.format(
len(avail_actions), ZERG_ABILITIES[ab][0]))
# print(ZERG_ABILITIES[ab][0])
print('Avail unit num: {}, select {}'.format(
len(avail_selections),
selection_units[:sum([(i != 600)
for i in selection_units])]))
print('Loss endpoints: {}'.format(loss))
# update & step the replay
last_pb = pb_obs
last_game_info = game_info
controller.step(1) # step_mul
controller.quit()
def __init__(self):
# Start the SC2 process.
self._run_config = run_configs.get()
self._process = self._run_config.start()
self._controller = self._process.controller
self._saved_maps = set()
def __init__(
self, # pylint: disable=invalid-name
_only_use_kwargs=None,
map_name=None,
players=None,
agent_race=None, # deprecated
bot_race=None, # deprecated
difficulty=None, # deprecated
screen_size_px=None, # deprecated
minimap_size_px=None, # deprecated
feature_screen_size=None,
feature_screen_width=None,
feature_screen_height=None,
feature_minimap_size=None,
feature_minimap_width=None,
feature_minimap_height=None,
rgb_screen_size=None,
rgb_screen_width=None,
rgb_screen_height=None,
rgb_minimap_size=None,
rgb_minimap_width=None,
rgb_minimap_height=None,
action_space=None,
camera_width_world_units=None,
discount=1.,
visualize=False,
step_mul=None,
save_replay_episodes=0,
replay_dir=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None,
use_feature_units=False,
random_seed=None):
"""Create a SC2 Env.
You must pass a resolution that you want to play at. You can send either
feature layer resolution or rgb resolution or both. If you send both you
must also choose which to use as your action space. Regardless of which you
choose you must send both the screen and minimap resolutions.
For each of the 4 resolutions, either specify size or both width and
height. If you specify size then both width and height will take that value.
Args:
_only_use_kwargs: Don't pass args, only kwargs.
map_name: Name of a SC2 map. Run bin/map_list to get the full list of
known maps. Alternatively, pass a Map instance. Take a look at the
docs in maps/README.md for more information on available maps.
players: A list of Agent and Bot instances that specify who will play.
agent_race: Deprecated. Use players instead.
bot_race: Deprecated. Use players instead.
difficulty: Deprecated. Use players instead.
screen_size_px: Deprecated. Use feature_screen_... instead.
minimap_size_px: Deprecated. Use feature_minimap_... instead.
feature_screen_size: Sets feature_screen_width and feature_screen_width.
feature_screen_width: The width of the feature layer screen observation.
feature_screen_height: The height of the feature layer screen observation.
feature_minimap_size: Sets feature_minimap_width and
feature_minimap_height.
feature_minimap_width: The width of the feature layer minimap observation.
feature_minimap_height: The height of the feature layer minimap
observation.
rgb_screen_size: Sets rgb_screen_width and rgb_screen_height.
rgb_screen_width: The width of the rgb screen observation.
rgb_screen_height: The height of the rgb screen observation.
rgb_minimap_size: Sets rgb_minimap_width and rgb_minimap_height.
rgb_minimap_width: The width of the rgb minimap observation.
rgb_minimap_height: The height of the rgb minimap observation.
action_space: If you pass both feature and rgb sizes, then you must also
specify which you want to use for your actions as an ActionSpace enum.
camera_width_world_units: The width of your screen in world units. If your
feature_screen=(64, 48) and camera_width is 24, then each px
represents 24 / 64 = 0.375 world units in each of x and y. It'll then
represent a camera of size (24, 0.375 * 48) = (24, 18) world units.
discount: Returned as part of the observation.
visualize: Whether to pop up a window showing the camera and feature
layers. This won't work without access to a window manager.
step_mul: How many game steps per agent step (action/observation). None
means use the map default.
save_replay_episodes: Save a replay after this many episodes. Default of 0
means don't save replays.
replay_dir: Directory to save replays. Required with save_replay_episodes.
game_steps_per_episode: Game steps per episode, independent of the
step_mul. 0 means no limit. None means use the map default.
score_index: -1 means use the win/loss reward, >=0 is the index into the
score_cumulative with 0 being the curriculum score. None means use
the map default.
score_multiplier: How much to multiply the score by. Useful for negating.
use_feature_units: Whether to include feature unit data in observations.
random_seed: Random number seed to use when initializing the game. This
lets you run repeatable games/tests.
Raises:
ValueError: if the agent_race, bot_race or difficulty are invalid.
ValueError: if too many players are requested for a map.
ValueError: if the resolutions aren't specified correctly.
DeprecationWarning: if screen_size_px or minimap_size_px are sent.
DeprecationWarning: if agent_race, bot_race or difficulty are sent.
"""
if _only_use_kwargs:
raise ValueError(
"All arguments must be passed as keyword arguments.")
if screen_size_px or minimap_size_px:
raise DeprecationWarning(
"screen_size_px and minimap_size_px are deprecated. Use the feature "
"or rgb variants instead. Make sure to check your observations too "
"since they also switched from screen/minimap to feature and rgb "
"variants.")
if agent_race or bot_race or difficulty:
raise DeprecationWarning(
"Explicit agent and bot races are deprecated. Pass an array of "
"sc2_env.Bot and sc2_env.Agent instances instead.")
if not players:
players = [
Agent(Race.random),
Bot(Race.random, Difficulty.very_easy)
]
for p in players:
if not isinstance(p, (Agent, Bot)):
raise ValueError(
"Expected players to be of type Agent or Bot. Got: %s." %
p)
self._num_players = sum(1 for p in players if isinstance(p, Agent))
self._players = players
if not 1 <= len(players) <= 2 or not 1 <= self._num_players <= 2:
raise ValueError("Only 1 or 2 players is supported at the moment.")
feature_screen_px = features.point_from_size_width_height(
feature_screen_size, feature_screen_width, feature_screen_height)
feature_minimap_px = features.point_from_size_width_height(
feature_minimap_size, feature_minimap_width,
feature_minimap_height)
rgb_screen_px = features.point_from_size_width_height(
rgb_screen_size, rgb_screen_width, rgb_screen_height)
rgb_minimap_px = features.point_from_size_width_height(
rgb_minimap_size, rgb_minimap_width, rgb_minimap_height)
if bool(feature_screen_px) != bool(feature_minimap_px):
raise ValueError("Must set all the feature layer sizes.")
if bool(rgb_screen_px) != bool(rgb_minimap_px):
raise ValueError("Must set all the rgb sizes.")
if not feature_screen_px and not rgb_screen_px:
raise ValueError("Must set either the feature layer or rgb sizes.")
if rgb_screen_px and (rgb_screen_px.x < rgb_minimap_px.x
or rgb_screen_px.y < rgb_minimap_px.y):
raise ValueError(
"Screen (%s) can't be smaller than the minimap (%s)." %
(rgb_screen_px, rgb_minimap_px))
if feature_screen_px and rgb_screen_px and not action_space:
raise ValueError(
"You must specify the action space if you have both observations."
)
if save_replay_episodes and not replay_dir:
raise ValueError("Missing replay_dir")
self._map = maps.get(map_name)
if self._map.players and self._num_players > self._map.players:
raise ValueError(
"Map only supports %s players, but trying to join with %s" %
(self._map.players, self._num_players))
self._discount = discount
self._step_mul = step_mul or self._map.step_mul
self._save_replay_episodes = save_replay_episodes
self._replay_dir = replay_dir
self._total_steps = 0
self._random_seed = random_seed
if score_index is None:
self._score_index = self._map.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = self._map.score_multiplier
else:
self._score_multiplier = score_multiplier
self._last_score = None
self._episode_length = game_steps_per_episode
if self._episode_length is None:
self._episode_length = self._map.game_steps_per_episode
self._episode_steps = 0
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
interface = sc_pb.InterfaceOptions(raw=(visualize
or use_feature_units),
score=True)
if feature_screen_px:
interface.feature_layer.width = camera_width_world_units or 24
feature_screen_px.assign_to(interface.feature_layer.resolution)
feature_minimap_px.assign_to(
interface.feature_layer.minimap_resolution)
if rgb_screen_px:
rgb_screen_px.assign_to(interface.render.resolution)
rgb_minimap_px.assign_to(interface.render.minimap_resolution)
if self._num_players == 1:
self._launch_sp(interface)
else:
self._launch_mp(interface)
game_info = self._controllers[0].game_info()
static_data = self._controllers[0].data()
if game_info.options.render != interface.render:
logging.warning(
"Actual interface options don't match requested options:\n"
"Requested:\n%s\n\nActual:\n%s", interface, game_info.options)
self._features = features.Features(game_info=game_info,
action_space=action_space,
use_feature_units=use_feature_units)
if visualize:
self._renderer_human = renderer_human.RendererHuman()
self._renderer_human.init(game_info, static_data)
else:
self._renderer_human = None
self._episode_count = 0
self._obs = None
self._state = environment.StepType.LAST # Want to jump to `reset`.
logging.info("Environment is ready.")
def start_game(self, show_cloaked=True, disable_fog=False, players=2):
"""Start a multiplayer game with options."""
self._disable_fog = disable_fog
run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
map_inst = maps.get("Flat64")
self._map_data = map_inst.data(run_config)
self._ports = portspicker.pick_unused_ports(4) if players == 2 else []
self._sc2_procs = [
run_config.start(extra_ports=self._ports, want_rgb=False)
for _ in range(players)
]
self._controllers = [p.controller for p in self._sc2_procs]
if players == 2:
for c in self._controllers: # Serial due to a race condition on Windows.
c.save_map(map_inst.path, self._map_data)
self._interface = sc_pb.InterfaceOptions()
self._interface.raw = True
self._interface.raw_crop_to_playable_area = True
self._interface.show_cloaked = show_cloaked
self._interface.score = False
self._interface.feature_layer.width = 24
self._interface.feature_layer.resolution.x = 64
self._interface.feature_layer.resolution.y = 64
self._interface.feature_layer.minimap_resolution.x = 64
self._interface.feature_layer.minimap_resolution.y = 64
create = sc_pb.RequestCreateGame(
random_seed=1,
disable_fog=self._disable_fog,
local_map=sc_pb.LocalMap(map_path=map_inst.path))
for _ in range(players):
create.player_setup.add(type=sc_pb.Participant)
if players == 1:
create.local_map.map_data = self._map_data
create.player_setup.add(type=sc_pb.Computer,
race=sc_common.Random,
difficulty=sc_pb.VeryEasy)
join = sc_pb.RequestJoinGame(race=sc_common.Protoss,
options=self._interface)
if players == 2:
join.shared_port = 0 # unused
join.server_ports.game_port = self._ports[0]
join.server_ports.base_port = self._ports[1]
join.client_ports.add(game_port=self._ports[2],
base_port=self._ports[3])
self._controllers[0].create_game(create)
self._parallel.run((c.join_game, join) for c in self._controllers)
self._info = self._controllers[0].game_info()
self._features = features.features_from_game_info(self._info,
use_raw_units=True)
self._map_size = point.Point.build(self._info.start_raw.map_size)
self.in_game = True
self.step() # Get into the game properly.
def _setup(self,
player_setup,
map_name,
screen_size_px=(64, 64),
minimap_size_px=(64, 64),
camera_width_world_units=None,
discount=1.,
visualize=False,
step_mul=None,
save_replay_episodes=0,
replay_dir=None,
game_steps_per_episode=None,
score_index=None,
score_multiplier=None):
if save_replay_episodes and not replay_dir:
raise ValueError("Missing replay_dir")
self._map = maps.get(map_name)
self._discount = discount
self._step_mul = step_mul or self._map.step_mul
self._save_replay_episodes = save_replay_episodes
self._replay_dir = replay_dir
self._total_steps = 0
if score_index is None:
self._score_index = self._map.score_index
else:
self._score_index = score_index
if score_multiplier is None:
self._score_multiplier = self._map.score_multiplier
else:
self._score_multiplier = score_multiplier
self._last_score = None
self._episode_length = (game_steps_per_episode
or self._map.game_steps_per_episode)
self._episode_steps = 0
self._run_config = run_configs.get()
self._parallel = run_parallel.RunParallel() # Needed for multiplayer.
screen_size_px = point.Point(*screen_size_px)
minimap_size_px = point.Point(*minimap_size_px)
interface = sc_pb.InterfaceOptions(
raw=visualize,
score=True,
feature_layer=sc_pb.SpatialCameraSetup(
width=camera_width_world_units or 24))
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
self._launch(interface, player_setup)
game_info = self._controllers[0].game_info()
static_data = self._controllers[0].data()
self._features = features.Features(game_info)
if visualize:
self._renderer_human = renderer_human.RendererHuman()
self._renderer_human.init(game_info, static_data)
else:
self._renderer_human = None
self._episode_count = 0
self._obs = None
self._state = environment.StepType.LAST # Want to jump to `reset`.
logging.info("Environment is ready.")
def mnet_v6d6_run_ppo2_loss_test():
mycfg = {
'test': False,
'use_self_fed_heads': False,
'use_value_head': True,
'use_loss_type': 'rl_ppo2',
'use_lstm': True,
'lstm_cell_type': 'lstm',
'lstm_layer_norm': True,
'batch_size': 8,
'rollout_len': 4,
'lstm_duration': 1,
'nlstm': 256,
'hs_len': 256 * 2,
'weight_decay': 0.0005,
'arg_scope_type': 'mnet_v6_type_a',
'use_base_mask': True,
'vec_embed_version': 'v3',
'last_act_embed_version': 'v2',
'zstat_embed_version': 'v4d1',
'zstat_index_base_wavelen': 555.6,
'trans_version': 'v4',
'use_astar_glu': True,
'use_astar_func_embed': True,
'n_v': 5,
'lam': 0.8,
'gather_batch': True,
'merge_pi': False,
'distillation': True,
'value_net_version': 'trans_v1',
}
converter = PB2AllConverter(dict_space=True,
zmaker_version='v5',
zstat_data_src=FLAGS.zstat_data_src,
game_version=FLAGS.game_version,
sort_executors='v1')
ob_space, ac_space = converter.space.spaces
# build the net
nc = net_config_cls(ob_space, ac_space, **mycfg)
nc.reward_weights = np.ones(shape=nc.reward_weights_shape,
dtype=np.float32)
inputs = net_inputs_placeholders_fun(nc)
keys = list(inputs.X.keys())
for key in keys:
inputs.X['OPPO_' + key] = inputs.X[key]
out = net_build_fun(inputs, nc, scope='mnet_v6d6_rl_ppo2_loss')
converter.reset(replay_name=FLAGS.replay_name,
player_id=FLAGS.player_id,
mmr=6000,
map_name=FLAGS.map_name)
run_config = run_configs.get()
replay_data = run_config.replay_data(
path.join(FLAGS.replay_dir, FLAGS.replay_name + '.SC2Replay'))
with run_config.start(version=FLAGS.game_version) as controller:
replay_info = controller.replay_info(replay_data)
print(replay_info)
controller.start_replay(
sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=None,
options=get_replay_actor_interface(
FLAGS.map_name),
observed_player_id=FLAGS.player_id,
disable_fog=False))
controller.step()
last_pb = None
last_game_info = None
sess = tf.Session()
tf.global_variables_initializer().run(session=sess)
while True:
pb_obs = controller.observe()
game_info = controller.game_info()
if last_pb is None:
last_pb = pb_obs
last_game_info = game_info
continue
if pb_obs.player_result:
# episode end, the zstat to this extent is what we need
break
# pb2all: X, A, weights as data
data = converter.convert(pb=(last_pb, last_game_info),
next_pb=(pb_obs, game_info))
if data:
feed_dict = {}
X, A = data[0][0]
for key in X:
feed_dict[inputs.X[key]] = [X[key]] * mycfg['batch_size']
# if 'OPPO_'+key in inputs.X:
# feed_dict[inputs.X['OPPO_'+key]] = [X[key]] * mycfg['batch_size']
for key in A:
feed_dict[inputs.A[key]] = [A[key]] * mycfg['batch_size']
feed_dict[inputs.neglogp[key]] = [0] * mycfg['batch_size']
feed_dict[inputs.flatparam[key]] = np.zeros(
shape=inputs.flatparam[key].shape)
feed_dict[inputs.r] = [[0] * nc.n_v] * mycfg['batch_size']
feed_dict[inputs.discount] = [1] * mycfg['batch_size']
feed_dict[inputs.S] = [
np.array([0] * mycfg['hs_len'], dtype=np.float32)
] * mycfg['batch_size']
feed_dict[inputs.M] = [np.array(1, dtype=np.bool)
] * mycfg['batch_size']
loss = sess.run([
out.loss.pg_loss, out.loss.value_loss,
out.loss.entropy_loss, out.loss.loss_endpoints
], feed_dict)
print('Loss endpoints: {}'.format(loss))
# update and step the replay
last_pb = pb_obs
last_game_info = game_info
controller.step(1) # step_mul
def test_multi_player(agents, disable_fog):
players = 2
if len(agents) == 2:
agent1, agent2 = agents
run_config = run_configs.get()
parallel = run_parallel.RunParallel()
map_inst = maps.get(FLAGS.map)
screen_size_px = point.Point(64, 64)
minimap_size_px = point.Point(32, 32)
interface = sc_pb.InterfaceOptions(raw=True, score=True)
screen_size_px.assign_to(interface.feature_layer.resolution)
minimap_size_px.assign_to(interface.feature_layer.minimap_resolution)
# Reserve a whole bunch of ports for the weird multiplayer implementation.
ports = [portpicker.pick_unused_port() for _ in range(1 + players * 2)]
print("Valid Ports: %s", ports)
# Actually launch the game processes.
print("start")
sc2_procs = [run_config.start(extra_ports=ports) for _ in range(players)]
controllers = [p.controller for p in sc2_procs]
try:
# Save the maps so they can access it.
map_path = os.path.basename(map_inst.path)
print("save_map")
parallel.run((c.save_map, map_path, run_config.map_data(map_inst.path))
for c in controllers)
# Create the create request.
real_time = True
create = sc_pb.RequestCreateGame(
local_map=sc_pb.LocalMap(map_path=map_path), realtime=real_time)
for _ in range(players):
create.player_setup.add(type=sc_pb.Participant)
# Create the join request.
join1 = sc_pb.RequestJoinGame(race=races[FLAGS.agent1_race],
options=interface)
join1.shared_port = ports.pop()
join1.server_ports.game_port = ports.pop()
join1.server_ports.base_port = ports.pop()
join1.client_ports.add(game_port=ports.pop(), base_port=ports.pop())
join2 = copy.copy(join1)
join2.race = races[FLAGS.agent2_race]
# This is where actually game plays
# Create and Join
print("create")
controllers[0].create_game(create)
print("join")
parallel.run((c.join_game, join)
for c, join in zip(controllers, [join1, join2]))
controllers[0]._client.send(debug=sc_pb.RequestDebug(
debug=[debug_pb2.DebugCommand(game_state=1)]))
if disable_fog[0]:
controllers[0].disable_fog()
if disable_fog[1]:
controllers[1].disable_fog()
print("run")
game_info = controllers[0].game_info()
extractors = features.Features(game_info)
for game_loop in range(1, 100000): # steps per episode
# Step the game
step_mul = FLAGS.step_mul
if not real_time:
parallel.run((c.step, step_mul) for c in controllers)
else:
time.sleep(FLAGS.sleep_time)
# Observe
obs = parallel.run(c.observe for c in controllers)
agent_obs = [extractors.transform_obs(o.observation) for o in obs]
game_info = [None for c in controllers]
if not any(o.player_result for o in obs): # Episode over.
game_info = parallel.run(c.game_info for c in controllers)
timesteps = tuple(
environment.TimeStep(step_type=0,
reward=0,
discount=0,
observation=o,
game_info=info)
for o, info in zip(agent_obs, game_info))
# Act
if agent1 is not None:
actions1 = agent1.step(timesteps[0])
else:
actions1 = []
actions2 = agent2.step(timesteps[1])
actions = [actions1, actions2]
funcs_with_args = [(c.acts, a)
for c, a in zip(controllers, actions)]
parallel.run(funcs_with_args)
# Done with the game.
print("leave")
parallel.run(c.leave for c in controllers)
finally:
print("quit")
# Done, shut down. Don't depend on parallel since it might be broken.
for c in controllers:
c.quit()
for p in sc2_procs:
p.close()
def main(argv):
import a3c.common.a3c
scenarios.load_scenarios()
run_config = run_configs.get()
interface = sc_pb.InterfaceOptions()
interface.raw = False
interface.score = True
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = FLAGS.screen_resolution
interface.feature_layer.resolution.y = FLAGS.screen_resolution
interface.feature_layer.minimap_resolution.x = 64
interface.feature_layer.minimap_resolution.y = 64
queue = FakeQueue()
#
shared.gamma_n = FLAGS.gamma ** FLAGS.n_step_return
env = helpers.get_env_wrapper(False)
s_space = env.observation_space.shape
none_state = np.zeros(s_space)
none_state = none_state.reshape(s_space)
replay_agent = Agent(env.action_space.n, t_queue=queue, none_state=none_state)
for fname in glob.glob(os.path.join(FLAGS.dir, '*.SC2Replay')):
replay_data = run_config.replay_data(fname)
start_replay = sc_pb.RequestStartReplay(
replay_data=replay_data,
options=interface,
disable_fog=True,
observed_player_id=1)
game_version = get_game_version(replay_data)
with run_config.start(game_version=game_version,
full_screen=False) as controller:
controller.start_replay(start_replay)
feat = features.Features(controller.game_info())
obs = controller.observe()
s = get_obs(env._input_layers, obs)
results = 0
last_reward = 0
while True:
actions = []
for a in obs.actions:
try:
temp = feat.reverse_action(a)
x = 0
y = 0
if temp[0] not in [0, 7]:
x = temp.arguments[1][0]
y = temp.arguments[1][1]
actions.append([env._actions.index(temp[0]), x ,y])
except ValueError:
pass
if len(actions) < 1:
try:
controller.step(FLAGS.step_mul)
except ProtocolError:
break;
obs = controller.observe()
s = get_obs(env._input_layers, obs)
continue
r = obs.observation.score.score
controller.step(FLAGS.step_mul)
obs = controller.observe()
s_ = get_obs(env._input_layers, obs)
if r == 0 and last_reward != 0:
s_ = None
print('Episode end')
results += 1
if not FLAGS.raw_rewards:
replay_agent.train(s, actions[0][0], actions[0][1], actions[0][2], r, s_)
else:
queue.put([s, actions[0][0], actions[0][1], actions[0][2], r, s_])
if obs.player_result:
break
if r == 0 and last_reward != 0:
last_reward = 0
else:
s = s_
last_reward = r
with gzip.open('./replay_info/info.gz', 'wb+') as outfile:
print('pushed: {}'.format(results))
#json.dump(queue.get(), outfile)
pickle.dump(queue.get(), outfile)
def test_replay_a_replay(self):
run_config = run_configs.get()
with run_config.start(want_rgb=False) as controller:
map_inst = maps.get("Flat64")
map_data = map_inst.data(run_config)
interface = sc_pb.InterfaceOptions(raw=True)
# Play a quick game to generate a replay.
create = sc_pb.RequestCreateGame(local_map=sc_pb.LocalMap(
map_path=map_inst.path, map_data=map_data))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Computer,
race=sc_common.Terran,
difficulty=sc_pb.VeryEasy)
join = sc_pb.RequestJoinGame(race=sc_common.Terran,
options=interface)
controller.create_game(create)
controller.join_game(join)
controller.step(100)
obs = controller.observe()
replay_data = controller.save_replay()
# Run through the replay the first time, verifying that it finishes, but
# wasn't recording a replay.
start_replay = sc_pb.RequestStartReplay(replay_data=replay_data,
map_data=map_data,
options=interface,
observed_player_id=1)
controller.start_replay(start_replay)
controller.step(1000)
obs2 = controller.observe()
self.assertEqual(obs.observation.game_loop,
obs2.observation.game_loop)
with self.assertRaises(protocol.ProtocolError):
controller.save_replay()
# Run through the replay a second time, verifying that it finishes, and
# *was* recording a replay.
start_replay.record_replay = True
controller.start_replay(start_replay)
controller.step(1000)
obs2 = controller.observe()
self.assertEqual(obs.observation.game_loop,
obs2.observation.game_loop)
replay_data2 = controller.save_replay()
# Make sure the replay isn't too small. Variance is fine but empty is not.
self.assertGreater(len(replay_data2), len(replay_data) * 0.8)
# Run through the replay a third time, verifying that it finishes, but
# still wasn't recording a replay.
start_replay.record_replay = False
controller.start_replay(start_replay)
controller.step(1000)
obs3 = controller.observe()
self.assertEqual(obs.observation.game_loop,
obs3.observation.game_loop)
with self.assertRaises(protocol.ProtocolError):
controller.save_replay()
