def test_load(self):
register(
model_id='test_load',
entry_point='rlcard3.models.pretrained_models:LeducHoldemNFSPModel'
)
models.load('test_load')
with self.assertRaises(ValueError):
load('test_random_make')
def _load_model(self):
"""
Load pretrained/rule model
:return: A Model object
"""
return models.load('mocsar-rule-v1',
num_players=self.game.get_player_num())
def _load_model(self):
''' Load pretrained/rule model
Returns:
model (Model): A Model object
'''
return models.load('leduc-holdem-cfr')
def _load_model(self):
''' Load pretrained/rule model
Returns:
model (Model): A Model object
'''
return models.load('uno-rule-v1')
def _load_model(self):
''' Load pretrained/rule model
Returns:
model (Model): A Model object
'''
assert False # FIXME: stub
return models.load('uno-rule-v1') # FIXME: stub
def _load_model(self):
"""
Load pretrained/rule model based on config.
:return: A Model object
"""
return models.load('mocsar-cfg',
num_players=self.game.get_player_num(),
action_num=self.game.get_action_num(),
state_shape=self.state_shape)
def get_agents(agents: Dict, nr_players: int, action_num: int,
state_shape: List):
"""
Initalize agents to play the game.
:param nr_players: Number of players, amount of agents generated
:param agents: Dictionary of agent_name: number of agents pairs
"""
agent_list = list()
i = 0
for agent_id, nr_agents in agents.items():
if agent_id == 'mocsar-nfsp-pytorch':
# Pre trained model from rlcard3.models.pretrained_models, NFSP has multiple (four) agents in it
# Here we directly load NFSP models from /models module
nfsp_agents = models.load(agent_id,
num_players=nr_players,
action_num=action_num,
state_shape=state_shape).agents
for j in range(nr_agents):
agent_list.append(nfsp_agents[j])
i += 1
if i >= nr_players:
return agent_list
for _ in range(nr_agents):
if agent_id in [
'mocsar-dqn-pytorch', 'mocsar-dqn-pytorchr',
'mocsar-nfsp-pytorch', 'mocsar-nfsp-pytorchm'
]:
# Pre trained model from rlcard3.models.pretrained_models, DQN
rule_agent = models.load(agent_id,
num_players=nr_players,
action_num=action_num,
state_shape=state_shape).agents[0]
else:
# Models from model_agents
rule_agent = load(agent_id=agent_id)
agent_list.append(rule_agent)
i += 1
if i >= nr_players:
return agent_list
''' Another example of loading a pre-trained NFSP model on Leduc Hold'em
Here, we directly load the model from model zoo
'''
import rlcard3
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3 import models
# Make environment
env = rlcard3.make('leduc-holdem')
# Set a global seed
set_global_seed(0)
# Here we directly load NFSP models from /models module
nfsp_agents = models.load('leduc-holdem-nfsp-pytorch').agents
# Evaluate the performance. Play with random agents.
evaluate_num = 10000
random_agent = RandomAgent(env.action_num)
env.set_agents([nfsp_agents[0], random_agent])
reward = tournament(env, evaluate_num)[0]
print('Average reward against random agent: ', reward)
save_plot_every = 1000
evaluate_num = 10000
episode_num = 10000
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_cfr_result/'
# Set a global seed
set_global_seed(0)
# Initilize CFR Agent
agent = CFRAgent(env)
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against pre-trained NFSP
eval_env.set_agents([agent, models.load('leduc-holdem-nfsp').agents[0]])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with NFSP agents.
if episode % evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
''' A toy example of playing against pretrianed AI on Leduc Hold'em
'''
import rlcard3
from rlcard3 import models
from rlcard3.agents.leduc_holdem_human_agent import HumanAgent
from rlcard3.utils.utils import print_card
# Make environment and enable human mode
# Set 'record_action' to True because we need it to print results
env = rlcard3.make('leduc-holdem', config={'record_action': True})
human_agent = HumanAgent(env.action_num)
cfr_agent = models.load('leduc-holdem-cfr').agents[0]
env.set_agents([human_agent, cfr_agent])
print(">> Leduc Hold'em pre-trained model")
while (True):
print(">> Start a new game")
trajectories, payoffs = env.run(is_training=False)
# If the human does not take the final action, we need to
# print other players action
final_state = trajectories[0][-1][-2]
action_record = final_state['action_record']
state = final_state['raw_obs']
_action_list = []
for i in range(1, len(action_record) + 1):
if action_record[-i][0] == state['current_player']:
break
_action_list.insert(0, action_record[-i])
''' Another example of loading a pre-trained NFSP model on Leduc Hold'em
Here, we directly load the model from model zoo
'''
import rlcard3
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3 import models
# Make environment
env = rlcard3.make('leduc-holdem')
# Set a global seed
set_global_seed(0)
# Here we directly load NFSP models from /models module
nfsp_agents = models.load('leduc-holdem-nfsp').agents
# Evaluate the performance. Play with random agents.
evaluate_num = 10000
random_agent = RandomAgent(env.action_num)
env.set_agents([nfsp_agents[0], random_agent])
reward = tournament(env, evaluate_num)[0]
print('Average reward against random agent: ', reward)
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3 import models
from rlcard3.utils.config_read import Config
# Make environment
env = rlcard3.make('mocsar')
# Get parameters
conf = Config('environ.properties')
evaluate_num = conf.get_int(section='cfg.compare', key='nr_games')
agent_str = conf.get_str(section='cfg.compare', key="agent_str")
nr_cards = conf.get_int(section='global', key='nr_cards')
# Set a global seed
#set_global_seed(0)
# Here we directly load NFSP models from /models module
dqn_agents = models.load(agent_str,
num_players=env.game.get_player_num(),
action_num=env.action_num,
state_shape=env.state_shape).agents
# Evaluate the performance. Play with random agents.
random_agent = RandomAgent(env.action_num)
env.game.set_game_params(num_players=4, num_cards=nr_cards)
env.set_agents([dqn_agents[0], random_agent, random_agent, random_agent])
reward = tournament(env, evaluate_num)[0]
print(
f'Average reward for {agent_str} against random agent: {reward}, cards: {nr_cards} '
)