def decide_chow(self, player, new_tile, choices, neighbors, game):
self.begin_decision()
fixed_hand, hand = player.fixed_hand, player.hand
if self.display_step:
self.print_game_board(fixed_hand, hand, neighbors, game)
self.print_msg("Someone just discarded a %s."%new_tile.symbol)
pg_model = get_MJPGFitted(self.pg_model_path)
state = utils.extended_dnn_encode_state(player, neighbors, cpk_tile = new_tile)
valid_actions = [34 + decisions_.index("no_action")]
for choice in choices:
valid_actions.append(34 + decisions_.index("chow_%d"%(choice)))
action_filter = np.zeros(n_decisions)
action_filter[valid_actions] = 1
action = None
if not self.skip_history and self.history_waiting:
self.update_transition(state, REWARD_NON_TERMINAL, action_filter)
while True:
if action is not None and not self.skip_history:
self.update_history(state, action, action_filter)
self.update_transition(state, REWARD_INVALID_DECISION, action_filter)
action, value = pg_model.choose_action(state, action_filter = action_filter, return_value = True, strict_filter = True)
if action in valid_actions:
break
elif not self.is_train:
action = random.choice(valid_actions)
break
if not self.skip_history:
self.update_history(state, action, action_filter)
self.end_decision()
if action == 34 + decisions_.index("no_action"):
self.print_msg("%s chooses not to Chow %s [%.2f]."%(self.player_name, new_tile.symbol, value))
return False, None
else:
chow_tiles_tgstrs = []
chow_tiles_str = ""
choice = int(decisions_[action - 34].split("_")[1])
for i in range(choice - 1, choice + 2):
neighbor_tile = new_tile.generate_neighbor_tile(i)
chow_tiles_str += neighbor_tile.symbol
chow_tiles_tgstrs.append(neighbor_tile.get_display_name(game.lang_code, is_short = False))
self.print_msg("%s chooses to Chow %s [%.2f]."%(self.player_name, chow_tiles_str, value))
if game.lang_code is not None:
game.add_notification(get_text(game.lang_code, "NOTI_CHOOSE_CHOW")%(self.player_name, ",".join(chow_tiles_tgstrs)))
return True, choice
def decide_drop_tile(self, player, new_tile, neighbors, game):
self.begin_decision()
fixed_hand, hand = player.fixed_hand, player.hand
state = utils.extended_dnn_encode_state(player,
neighbors,
new_tile=new_tile)
if self.display_step:
self.print_game_board(fixed_hand, hand, neighbors, game, new_tile)
pg_model = get_MJPGFitted(self.pg_model_path)
valid_actions = []
tiles = player.hand if new_tile is None else player.hand + [new_tile]
for tile in tiles:
valid_actions.append(Tile.convert_tile_index(tile))
action_filter = np.zeros(n_decisions)
action_filter[valid_actions] = 1
action = None
if not self.skip_history and self.history_waiting:
self.update_transition(state, REWARD_NON_TERMINAL, action_filter)
while True:
if action is not None and not self.skip_history:
self.update_history(state, action, action_filter)
self.update_transition(state, REWARD_INVALID_DECISION,
action_filter)
action, value = pg_model.choose_action(state,
action_filter=action_filter,
return_value=True,
strict_filter=True)
if action in valid_actions:
break
elif not self.is_train:
action = random.choice(valid_actions)
break
if not self.skip_history:
self.update_history(state, action, action_filter)
drop_tile = Tile.convert_tile_index(action)
self.print_msg(
"%s [%s] chooses to drop %s. [%.2f]" %
(self.player_name, display_name, drop_tile.symbol, value))
self.end_decision(True)
if game.lang_code is not None:
game.add_notification(
get_text(game.lang_code, "NOTI_CHOOSE_DISCARD") %
(self.player_name,
drop_tile.get_display_name(game.lang_code, is_short=False)))
return drop_tile
def decide_kong(self, player, new_tile, kong_tile, location, src, neighbors, game):
self.begin_decision()
fixed_hand, hand = player.fixed_hand, player.hand
if self.display_step:
self.print_game_board(fixed_hand, hand, neighbors, game, new_tile)
if src == "steal":
self.print_msg("Someone just discarded a %s."%kong_tile.symbol)
elif src == "draw":
self.print_msg("You just drew a %s"%kong_tile.symbol)
elif src == "existing":
self.print_msg("You have 4 %s in hand"%kong_tile.symbol)
if location == "fixed_hand":
location = "fixed hand"
else:
location = "hand"
pg_model = get_MJPGFitted(self.pg_model_path)
state = utils.extended_dnn_encode_state(player, neighbors, cpk_tile = kong_tile)
valid_actions = [34 + decisions_.index("pong"), 34 + decisions_.index("no_action")]
action_filter = np.zeros(n_decisions)
action_filter[valid_actions] = 1
action = None
if not self.skip_history and self.history_waiting:
self.update_transition(state, REWARD_NON_TERMINAL, action_filter)
while True:
if action is not None and not self.skip_history:
self.update_history(state, action, action_filter)
self.update_transition(state, REWARD_INVALID_DECISION, action_filter)
action, value = pg_model.choose_action(state, action_filter = action_filter, return_value = True, strict_filter = True)
if action in valid_actions:
break
elif not self.is_train:
action = random.choice(valid_actions)
break
if not self.skip_history:
self.update_history(state, action, action_filter)
self.end_decision()
if action == 34 + decisions_.index("no_action"):
self.print_msg("%s [%s] chooses to form a Kong %s%s%s%s [%.2f]."%(self.player_name, display_name, kong_tile.symbol, kong_tile.symbol, kong_tile.symbol, kong_tile.symbol, value))
if game.lang_code is not None:
game.add_notification(get_text(game.lang_code, "NOTI_CHOOSE_KONG")%(self.player_name, kong_tile.get_display_name(game.lang_code, is_short = False)))
return True
else:
self.print_msg("%s [%s] chooses not to form a Kong %s%s%s%s [%.2f]."%(self.player_name, display_name, kong_tile.symbol, kong_tile.symbol, kong_tile.symbol, kong_tile.symbol, value))
return False
def update_transition(self, state_, reward = 0, action_filter_ = None):
if not self.is_train:
return
if not self.history_waiting:
raise Exception("the network is NOT waiting for a transition")
self.history_waiting = False
pg_model = get_MJPGFitted(self.pg_model_path)
pg_model.store_transition(self.pg_model_history["state"], self.pg_model_history["action"], reward, self.pg_model_history["action_filter"], self.pg_model_history["heuristics_action"])
def test(args):
global game_record_count
args = parse_args(args)
if args.action == "train":
if args.save_name is None:
response = input(
"You have not entered save_name, are you sure? [y/n] ").lower(
)
if response != "y":
exit(-1)
if args.model_dir is None:
args.model_dir = pg_model_path
else:
trainer_models["pgf"]["parameters"][
"pg_model_path"] = args.model_dir
trainer_models["pgfr"]["parameters"][
"pg_model_path"] = args.model_dir
freq_model_save = args.n_episodes // 10
elif args.action in ["test", "play"]:
if args.model_dir is None:
raise Exception("model_dir must be given to test/play")
model = get_MJPGFitted(args.model_dir, **pg_model_paras)
players = []
i = 0
for model_tag in trainer_conf:
if args.action == "play":
player = Player.Player(MoveGenerator.Human, player_name=names[i])
else:
player = Player.Player(trainer_models[model_tag]["class"],
player_name=names[i],
**trainer_models[model_tag]["parameters"])
players.append(player)
i += 1
pg_player = Player.Player(MoveGenerator.PGFGenerator if model_flag == "pgf"
else MoveGenerator.PGFRGenerator,
player_name=names[i],
pg_model_path=args.model_dir,
skip_history=False,
is_train=args.action == "train",
display_step=args.action == "play")
players.append(pg_player)
if args.action != "play":
signal.signal(signal.SIGINT, signal_handler)
game, shuffled_players, last_saved = None, None, -1
for i in range(args.n_episodes):
if EXIT_FLAG:
break
if i % freq_shuffle_players == 0:
shuffled_players = random.sample(players, k=4)
game = Game.Game(shuffled_players)
winner, losers, penalty = game.start_game()
if args.action == "train":
model.learn(display_cost=(i + 1) % game_record_size == 0)
index = game_record_count % game_record_size
game_record[index, :, :] = np.zeros((4, 2))
game_record_count += 1
if winner is not None:
winner_id = players.index(winner)
game_record[index, winner_id, 0] = 1
for loser in losers:
loser_id = players.index(loser)
game_record[index, loser_id, 1] = 1
if (i + 1) % game_record_size == 0:
print(
"#%5d: %.2f%%/%.2f%%\t%.2f%%/%.2f%%\t%.2f%%/%.2f%%\t%.2f%%/%.2f%%"
% (i + 1, game_record[:, 0, 0].mean() * 100,
game_record[:, 0, 1].mean() * 100,
game_record[:, 1, 0].mean() * 100,
game_record[:, 1, 1].mean() * 100,
game_record[:, 2, 0].mean() * 100,
game_record[:, 2, 1].mean() * 100,
game_record[:, 3, 0].mean() * 100,
game_record[:, 3, 1].mean() * 100))
'''
if args.action == "train" and args.save_name is not None and (i+1) % freq_model_save == 0:
last_saved = i
path = args.save_name.rstrip("/") + "_%d"%(i + 1)
utils.makesure_dir_exists(path)
model.save(path)
'''
if args.action == "train" and args.save_name is not None:
if last_saved < args.n_episodes - 1:
path = args.save_name.rstrip("/") + "_%d" % args.n_episodes
utils.makesure_dir_exists(path)
model.save(path)