self._update_prev_rounds(state,board_card,rround,r,a)
#Execute self bet in +Training
bet = self.state_controller.bet()
#Determine action type
action_type = self._get_action_type(game, bet)
print(" Bet:",bet,"Type:",action_type)
#Set action (+amount) for server
if action_type == acpc.ActionType.RAISE and game.get_betting_type() == acpc.BettingType.NO_LIMIT:
self.set_next_action(action_type, bet + self.contrib[self.player][-1])
else:
self.set_next_action(action_type)
self.player = 1 - self.player
def on_game_finished(self, game, match_state):
print("==========")
print()
if __name__ == "__main__":
if len(sys.argv) < 4:
print("Usage {game_file_path} {dealer_hostname} {dealer_port}")
sys.exit(1)
client = acpc.Client(sys.argv[1], sys.argv[2], sys.argv[3])
L = Libratus()
client.play(L)
def _run_agent(args):
client = acpc.Client(args[0], '127.0.1.1', args[1])
client.play(StrategyAgent(args[2]))
strategy[line_split[0]] = [
float(probStr) for probStr in line_split[1:4]
]
self.strategy = strategy
def on_game_start(self, game):
pass
def on_next_turn(self, game, match_state, is_acting_player):
if not is_acting_player:
return
info_set = _get_info_set(game, match_state)
node_strategy = self.strategy[info_set]
selected_action = select_action(node_strategy)
self.set_next_action(selected_action)
def on_game_finished(self, game, match_state):
pass
if __name__ == "__main__":
if len(sys.argv) < 5:
print(
"Usage {game_file_path} {strategy_file_path} {dealer_hostname} {dealer_port}"
)
sys.exit(1)
client = acpc.Client(sys.argv[1], sys.argv[3], sys.argv[4])
client.play(StrategyAgent(sys.argv[2]))
def evaluate_agent(self, test_spec):
portfolio_name = test_spec['portfolio_name']
portfolio_directory = '%s/%s' % (PORTFOLIOS_DIRECTORY, portfolio_name)
game_file_path = test_spec['game_file_path']
game = acpc.read_game_file(game_file_path)
if game.get_num_players() != 2:
raise AttributeError('Only games with 2 players are supported')
response_strategy_paths = []
opponent_names = []
opponent_script_paths = []
for file in os.listdir(portfolio_directory):
if file.endswith('-response.strategy'):
response_strategy_paths += [file]
elif file.endswith('.sh') and not file.startswith(portfolio_name):
opponent_names += [file[:-len('.sh')]]
opponent_script_paths += [
'%s/%s' % (portfolio_directory, file)
]
portfolio_size = len(response_strategy_paths)
logs_dir = '/'.join([GAME_LOGS_DIRECTORY, portfolio_name])
if os.path.exists(logs_dir):
shutil.rmtree(logs_dir)
os.makedirs(logs_dir)
big_blind_size = get_big_blind_size(game)
env = os.environ.copy()
env['PATH'] = os.path.dirname(sys.executable) + ':' + env['PATH']
print()
for i in range(portfolio_size):
opponent_name = opponent_names[i]
logs_path = '%s/%s' % (logs_dir, opponent_name)
proc = subprocess.Popen([
START_DEALER_AND_OPPONENT_SCRIPT_PATH, game_file_path,
logs_path, opponent_name, opponent_script_paths[i],
portfolio_name
],
env=env,
stdout=subprocess.PIPE)
port_number = proc.stdout.readline().decode('utf-8').strip()
client = acpc.Client(game_file_path, '127.0.1.1', port_number)
full_response_strategy_paths = [
'%s/%s' % (portfolio_directory, s)
for s in response_strategy_paths
]
utility_estimator_args = test_spec[
'utility_estimator_args'] if 'utility_estimator_args' in test_spec else None
client.play(
ImplicitModellingAgent(
game_file_path,
full_response_strategy_paths,
utility_estimator_class=test_spec[
'utility_estimator_class'],
utility_estimator_args=utility_estimator_args))
scores_line = proc.stdout.readline().decode('utf-8').strip()
agent_score = float(scores_line.split(':')[1].split('|')[1])
agent_score_mbb_per_game = (agent_score /
NUM_EVAL_HANDS) * big_blind_size
print('%s vs %s: %s' %
(portfolio_name, opponent_name, agent_score_mbb_per_game))