121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import os
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import json
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import tensorflow as tf
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import sys
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import rlcard
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from tqdm import tqdm
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from rlcard.agents.nfsp_agent import NFSPAgent
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from rlcard.agents.dqn_agent import DQNAgent
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from math import log10
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# from rlcard.agents.random_agent import RandomAgent
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from rlcard.utils.utils import set_global_seed
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from rlcard.utils.logger import Logger
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from pretrained_models import LeducHoldemDQNModel1, LeducHoldemNFSPModel, LeducHoldemCFRModel, LeducHoldemRandomModel, LeducHoldemRuleModel, LeducHoldemDQNModel2
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class Tournament(object):
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def __init__(self,
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agent1,
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agent2,
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agent3,
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agent4,
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agent5,
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env_id,
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evaluate_num=10000):
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set_global_seed(0)
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self.env_id = env_id
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self.env1 = rlcard.make(env_id, allow_raw_data=True)
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self.env2 = rlcard.make(env_id, allow_raw_data=True)
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self.env3 = rlcard.make(env_id, allow_raw_data=True)
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self.agent1 = agent1.agents[0]
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self.agent2 = agent2.agents[0]
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self.agent3 = agent3.agents[0]
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self.agent4 = agent4.agents[0]
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self.agent5 = agent5.agents[0]
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self.evaluate_num = evaluate_num
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self.env1.set_agents([self.agent1, self.agent2])
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self.env2.set_agents([self.agent1, self.agent3])
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self.env3.set_agents([self.agent1, self.agent4])
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def competition(self):
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agent1_wins = 0
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agent2_wins = 0
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print("########## Play Against Random Agent ##########")
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for eval_episode in tqdm(range(self.evaluate_num)):
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_, payoffs = self.env1.run(is_training=False)
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agent1_wins += payoffs[0]
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agent2_wins += payoffs[1]
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agent1_rate = agent1_wins / self.evaluate_num
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agent2_rate = agent2_wins / self.evaluate_num
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print("DQN Agent average peroformance:", agent1_rate)
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print("Random Agent avgerage performance:", agent2_rate)
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print("\n")
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print("########## Play Against Rule-based Agent ##########")
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agent1_wins = 0
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agent2_wins = 0
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for eval_episode in tqdm(range(self.evaluate_num)):
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_, payoffs = self.env2.run(is_training=False)
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agent1_wins += payoffs[0]
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agent2_wins += payoffs[1]
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agent1_rate = agent1_wins / self.evaluate_num
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agent2_rate = agent2_wins / self.evaluate_num
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print("DQN Agent average peroformance:", agent1_rate)
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print("Rule-based Agent avgerage performance:", agent2_rate)
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print("\n")
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agent1_wins = 0
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agent2_wins = 0
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print("########## Play Against CFR Agent ##########")
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for eval_episode in tqdm(range(self.evaluate_num)):
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_, payoffs = self.env2.run(is_training=False)
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agent1_wins += payoffs[0]
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agent2_wins += payoffs[1]
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agent1_rate = agent1_wins / self.evaluate_num
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agent2_rate = agent2_wins / self.evaluate_num
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print("DQN Agent average peroformance:", agent1_rate)
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print("CFR Agent avgerage performance:", agent2_rate)
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def evaluate(self):
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agents = [self.agent1, self.agent2, self.agent3, self.agent4, self.agent5]
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for a1 in agents:
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avg_performance = 0.0
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print("########### Evaluating "+ str(a1) +" #########")
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for a2 in agents:
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if a1 == a2:
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continue
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agent1_wins = 0
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env = rlcard.make(self.env_id, allow_raw_data=True)
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env.set_agents([a1, a2])
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for eval_episode in range(self.evaluate_num):
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_, payoffs = env.run(is_training=False)
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agent1_wins += payoffs[0]
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agent1_rate = agent1_wins / self.evaluate_num
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if agent1_rate > 0:
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avg_performance += 1.0
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print("Against "+str(a2)+":", agent1_rate)
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avg_performance /= len(agents)-1
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print("Average Performance:", avg_performance)
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print("\n")
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if __name__=='__main__':
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root_path = './models'
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agent1 = LeducHoldemDQNModel1(root_path)
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agent2 = LeducHoldemRandomModel(root_path)
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agent3 = LeducHoldemRuleModel()
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agent4 = LeducHoldemCFRModel(root_path)
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agent5 = LeducHoldemDQNModel2(root_path)
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t = Tournament(agent1, agent2, agent3, agent4, agent5, 'leduc-holdem')
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#t.competition()
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t.evaluate()
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