def test_load(self): register( model_id='test_load', entry_point='rlcard.models.pretrained_models:LeducHoldemCFRModel') models.load('test_load') with self.assertRaises(ValueError): load('test_random_make')
def test_register(self): register( model_id='test_reg', entry_point='rlcard.models.pretrained_models:LeducHoldemCFRModel') with self.assertRaises(ValueError): register(model_id='test_reg', entry_point= 'rlcard.models.pretrained_models:LeducHoldemCFRModel')
''' Register rule-based models or pre-trianed models ''' from rlcard.models.registration import register, load register( model_id = 'leduc-holdem-nfsp', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPModel') register( model_id = 'uno-rule-v1', entry_point='rlcard.models.uno_rule_models:UNORuleModelV1') register( model_id = 'badugi-rule-v1', entry_point='rlcard.models.badugi_rule_models:BadugiRuleModelV1')
from rlcard.models.registration import register, load import subprocess import sys from distutils.version import LooseVersion reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']) installed_packages = [r.decode().split('==')[0] for r in reqs.split()] if 'tensorflow' in installed_packages: import tensorflow as tf if LooseVersion(tf.__version__) < LooseVersion('1.14.0') \ or LooseVersion(tf.__version__) >= LooseVersion('2.0.0'): print('WAINING - RLCard supports Tensorflow >=1.14 and <2.0\nThe detected version is {} \nIf the models can not be loaded, please install Tensorflow via\n$ pip install rlcard[tensorflow]\n'.format(tf.__version__)) register( model_id = 'leduc-holdem-nfsp', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPModel') if 'torch' in installed_packages: register( model_id = 'leduc-holdem-nfsp-pytorch', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPPytorchModel') register( model_id = 'leduc-holdem-cfr', entry_point='rlcard.models.pretrained_models:LeducHoldemCFRModel') register( model_id = 'leduc-holdem-rule-v1', entry_point='rlcard.models.leducholdem_rule_models:LeducHoldemRuleModelV1')
''' Register rule-based models or pre-trianed models ''' from rlcard.models.registration import register, load import subprocess import sys reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']) installed_packages = [r.decode().split('==')[0] for r in reqs.split()] if 'tensorflow' in installed_packages: register( model_id='leduc-holdem-nfsp', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPModel') if 'torch' in installed_packages: register(model_id='leduc-holdem-nfsp-pytorch', entry_point= 'rlcard.models.pretrained_models:LeducHoldemNFSPPytorchModel') register(model_id='leduc-holdem-cfr', entry_point='rlcard.models.pretrained_models:LeducHoldemCFRModel') register( model_id='leduc-holdem-rule-v1', entry_point='rlcard.models.leducholdem_rule_models:LeducHoldemRuleModelV1') register( model_id='leduc-holdem-rule-v2', entry_point='rlcard.models.leducholdem_rule_models:LeducHoldemRuleModelV2') register(model_id='uno-rule-v1',
import subprocess import sys from packaging import version reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']) installed_packages = [r.decode().split('==')[0] for r in reqs.split()] if 'tensorflow' in installed_packages: import tensorflow as tf if version.parse(tf.__version__) < version.parse('1.14.0') \ or version.parse(tf.__version__) >= version.parse('2.0.0'): print( 'WAINING - RLCard supports Tensorflow >=1.14 and <2.0\nThe detected version is {} \nIf the models can not be loaded, please install Tensorflow via\n$ pip install rlcard[tensorflow]\n' .format(tf.__version__)) register( model_id='leduc-holdem-nfsp', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPModel') register(model_id='uno-nfsp', entry_point='rlcard.models.pretrained_models:UnoNFSPModel') if 'torch' in installed_packages: register(model_id='leduc-holdem-nfsp-pytorch', entry_point= 'rlcard.models.pretrained_models:LeducHoldemNFSPPytorchModel') register(model_id='leduc-holdem-cfr', entry_point='rlcard.models.pretrained_models:LeducHoldemCFRModel') register( model_id='leduc-holdem-rule-v1',
""" Register rule-based models or pre-trianed models """ from rlcard.models.registration import register, load register(model_id='tarot-rule-v1', entry_point='rlcard.models.tarot_rule_models:TAROTRuleModelV1') register(model_id='tarot-bid-rule-v1', entry_point='rlcard.models.tarot_bid_rule_models:TAROTBIDRuleModelV1') register(model_id='tarot-dog-rule-v1', entry_point='rlcard.models.tarot_dog_rule_models:TAROTDOGRuleModelV1')