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''' An example of solve Leduc Hold'em with CFR (chance sampling)
'''
import os
import argparse
import rlcard
from rlcard.agents import CFRAgent, RandomAgent
from rlcard.utils import set_seed, tournament, Logger, plot_curve
def train(args):
# Make environments, CFR only supports Leduc Holdem
env = rlcard.make('leduc-holdem', config={'seed': 0, 'allow_step_back':True})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0})
# Seed numpy, torch, random
set_seed(args.seed)
# Initilize CFR Agent
agent = CFRAgent(env, os.path.join(args.log_dir, 'cfr_model'))
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against random
eval_env.set_agents([agent, RandomAgent(num_actions=env.num_actions)])
# Start training
with Logger(args.log_dir) as logger:
for episode in range(args.num_episodes):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with Random agents.
if episode % args.evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep, tournament(eval_env, args.num_eval_games)[0])
# Get the paths
csv_path, fig_path = logger.csv_path, logger.fig_path
# Plot the learning curve
plot_curve(csv_path, fig_path, 'cfr')
if __name__ == '__main__':
parser = argparse.ArgumentParser("CFR example in RLCard")
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--num_episodes', type=int, default=5000)
parser.add_argument('--num_eval_games', type=int, default=2000)
parser.add_argument('--evaluate_every', type=int, default=100)
parser.add_argument('--log_dir', type=str, default='experiments/leduc_holdem_cfr_result/')
args = parser.parse_args()
train(args)
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