class CDQNAgent(Agent):
def __init__(self, role_id, weight_path):
def role2agent(role):
if role == 2:
return 'agent1'
elif role == 1:
return 'agent3'
else:
return 'agent2'
super().__init__(role_id)
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, (1000, 21, 256 + 256 * 2 + 120), 'Double',
(1000, 21), 0.99)
self.predictor = Predictor(OfflinePredictor(
PredictConfig(model=model,
session_init=SaverRestore(weight_path),
input_names=[
role2agent(role_id) + '/state',
role2agent(role_id) + '_comb_mask',
role2agent(role_id) + '/fine_mask'
],
output_names=[role2agent(role_id) + '/Qvalue'])),
num_actions=(1000, 21))
def intention(self, env):
handcards = env.get_curr_handcards()
last_two_cards = env.get_last_two_cards()
prob_state = env.get_state_prob()
intention = self.predictor.predict(handcards, last_two_cards,
prob_state)
return intention
def __init__(self, role_id, weight_path):
def role2agent(role):
if role == 2:
return 'agent1'
elif role == 1:
return 'agent3'
else:
return 'agent2'
super().__init__(role_id)
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, (1000, 21, 256 + 256 * 2 + 120), 'Double', (1000, 21), 0.99)
self.predictor = Predictor(OfflinePredictor(PredictConfig(
model=model,
session_init=SaverRestore(weight_path),
input_names=[role2agent(role_id) + '/state', role2agent(role_id) + '_comb_mask', role2agent(role_id) + '/fine_mask'],
output_names=[role2agent(role_id) + '/Qvalue'])), num_actions=(1000, 21))
def _setup_graph(self):
# self.lord_win_rate = tf.get_variable('lord_win_rate', shape=[], initializer=tf.constant_initializer(0.),
# trainable=False)
nr_proc = min(multiprocessing.cpu_count() // 2, 1)
self.predictors = {n: Predictor(
self.trainer.get_predictor([n + '/state:0', n + '_comb_mask:0', n + '/fine_mask:0'], [n + '/Qvalue:0'])) for
n in self.agent_names}
self.pred_funcs = [self.predictors] * nr_proc
def __init__(self, weight_path):
super().__init__()
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, (1000, 21, 256 + 256 * 2 + 120), 'Double', (1000, 21), 0.99)
self.predictors = {n: Predictor(OfflinePredictor(PredictConfig(
model=model,
session_init=SaverRestore(weight_path),
input_names=[n + '/state', n + '_comb_mask', n + '/fine_mask'],
output_names=[n + '/Qvalue'])), num_actions=(1000, 21)) for n in self.get_all_agent_names()}
def _setup_graph(self):
# self.lord_win_rate = tf.get_variable('lord_win_rate', shape=[], initializer=tf.constant_initializer(0.),
# trainable=False)
nr_proc = min(multiprocessing.cpu_count() // 2, 1)
self.pred_funcs = [
Predictor(
self.trainer.get_predictor(self.input_names,
self.output_names))
] * nr_proc
def _setup_graph(self):
self.curr_predictor = self.trainer.get_predictor([
self.agent_name + '/state:0', self.agent_name + '_comb_mask:0',
self.agent_name + '/fine_mask:0'
], [self.agent_name + '/Qvalue:0'])
self.predictors = {
n: Predictor(
self.trainer.get_predictor(
[n + '/state:0', n + '_comb_mask:0', n + '/fine_mask:0'],
[n + '/Qvalue:0']))
for n in self.player.get_all_agent_names()
}
