How to use the rlcard.make function in rlcard

def test_train(self):

        env = rlcard.make('leduc-holdem', allow_step_back=True)
        agent = CFRAgent(env)

        for _ in range(100):
            agent.train()

        state = {'obs': np.array([1., 1., 0., 0., 0., 0.]), 'legal_actions': [0,2]}
        action = agent.eval_step(state)

        self.assertIn(action, [0, 2])

# Example of using doudizhu environment
import rlcard
from rlcard.agents.random_agent import RandomAgent

# make environment
env = rlcard.make('simpletexasholdem')

print('############## Environment of Simple Texas Holdem Initilized ################')

env.test()

# set agents
agent_0 = RandomAgent()
agent_1 = RandomAgent()
agent_2 = RandomAgent()
env.set_agents([agent_0, agent_1, agent_2])

# seed everything
env.set_seed(0)
agent_0.set_seed(0)
agent_1.set_seed(0)
agent_2.set_seed(0)

''' An example of learning a NFSP Agent on Dou Dizhu
'''

import tensorflow as tf

import rlcard
from rlcard.agents.nfsp_agent import NFSPAgent
from rlcard.agents.random_agent import RandomAgent
from rlcard.utils.utils import set_global_seed
from rlcard.utils.logger import Logger

# Make environment
env = rlcard.make('mahjong')
eval_env = rlcard.make('mahjong')

# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 100
save_plot_every = 1000
evaluate_num = 5000
episode_num = 10000000

# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = 1000
norm_step = 1000

# The paths for saving the logs and learning curves
root_path = './experiments/mahjong_nfsp_result/'
log_path = root_path + 'log.txt'

# Avoid RuntimeError
    multiprocessing.freeze_support()

    # Set the number of process
    process_num = 8

    # Set episode_num
    episode_num = 10000

    # Assign tasks
    per_tasks = assign_task(episode_num, process_num)

    # Set game and make environment
    game = 'doudizhu'
    env = rlcard.make(game)

    # Set global seed
    set_global_seed(1)

    # Set up agents
    agent_num = env.player_num
    env.set_agents([RandomAgent(action_num=env.action_num)
                    for _ in range(agent_num)])

    # Set a global list to reserve trajectories
    manager = multiprocessing.Manager()
    trajectories_set = manager.list()

    # Generate Processes
    processes = []
    for p in range(process_num):

''' An example of learning a Deep-Q Agent on Texas No-Limit Holdem
'''

import tensorflow as tf

import rlcard
from rlcard.agents.dqn_agent import DQNAgent
from rlcard.agents.random_agent import RandomAgent
from rlcard.utils.utils import set_global_seed
from rlcard.utils.logger import Logger

# Make environment
env = rlcard.make('no-limit-holdem')
eval_env = rlcard.make('no-limit-holdem')

# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 1000
save_plot_every = 10000
evaluate_num = 10000
episode_num = 1000000

# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = 1000
norm_step = 100

# Set a global seed
set_global_seed(0)

''' An example of learning a Deep-Q Agent on Texas Limit Holdem
'''

import tensorflow as tf

import rlcard
from rlcard.agents.dqn_agent import DQNAgent
from rlcard.agents.random_agent import RandomAgent
from rlcard.utils.utils import set_global_seed
from rlcard.utils.logger import Logger

# Make environment
env = rlcard.make('limit-holdem')
eval_env = rlcard.make('limit-holdem')

# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 100
save_plot_every = 1000
evaluate_num = 10000
episode_num = 1000000

# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = 1000
norm_step = 100

# The paths for saving the logs and learning curves
root_path = './experiments/limit_holdem_dqn_result/'
log_path = root_path + 'log.txt'
csv_path = root_path + 'performance.csv'

''' An example of learning a NFSP Agent on Dou Dizhu
'''

import tensorflow as tf

import rlcard
from rlcard.agents.nfsp_agent import NFSPAgent
from rlcard.agents.random_agent import RandomAgent
from rlcard.utils.utils import set_global_seed
from rlcard.utils.logger import Logger

# Make environment
env = rlcard.make('doudizhu')
eval_env = rlcard.make('doudizhu')

# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 500
save_plot_every = 10000
evaluate_num = 1000
episode_num = 10000000

# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = 1000
norm_step = 1000

# Set a global seed
set_global_seed(0)

''' An example of learning a NFSP Agent on Leduc Hold'em
'''

import tensorflow as tf

import rlcard
from rlcard.agents.nfsp_agent import NFSPAgent
from rlcard.agents.random_agent import RandomAgent
from rlcard.utils.utils import set_global_seed
from rlcard.utils.logger import Logger

# Make environment
env = rlcard.make('leduc-holdem')
eval_env = rlcard.make('leduc-holdem')

# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 1000
save_plot_every = 10000
evaluate_num = 10000
episode_num = 10000000

# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = 1000
norm_step = 1000

# Set a global seed
set_global_seed(0)

if __name__ == '__main__':
    # Timer start
    start = time.time()

    # Avoid RuntimeError
    multiprocessing.freeze_support()

    # Set the number of process
    process_num = 8

    # Initialize process pool
    pool = multiprocessing.Pool(process_num)

    # Set game and make environment
    env = rlcard.make('doudizhu')

    # Set episode_num
    episode_num = 10000

    # Set global seed
    set_global_seed(1)

    # Set up agents
    agent_num = env.game.num_players
    env.set_agents([RandomAgent(action_num=env.action_num)
                    for _ in range(agent_num)])

    # Run game
    trajectories_set = []
    for episode in range(episode_num):