main_testing.py

''' DQN agent

The code is derived from https://github.com/dennybritz/reinforcement-learning/blob/master/DQN/dqn.py

Copyright (c) 2019 DATA Lab at Texas A&M University
Copyright (c) 2016 Denny Britz

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''



'''

This is a modified agent by Georg which uses tensorflow 2.3.0, keras 2.4.3, numpy 1.18.5 

'''


import tensorflow as tf
import keras
import os
import time


import agents.ddqn
import agents.a2c
import agents.doudizhu_rule_models as doudizhu_rule_models
import agents.simpledoudizhu_rule_models as simpledoudizhu_rule_models
import agents.random_agent as random_agent
from envs.utils import set_global_seed, tournament
import envs.logger as logger
from envs.env import Env
import envs.doudizhu as doudizhu
import envs.simpledoudizhu as simpledoudizhu


config = {  'allow_step_back':True,
            'allow_raw_data': False, 
            'record_action': True,
            'seed': 42,
            'single_agent_mode': False,
            'active_player': True}

# Make environment
eval_env = doudizhu.DoudizhuEnv(config)
env = simpledoudizhu.SimpleDoudizhuEnv(config)
#doudizhu.DoudizhuEnv(config)

evaluate_num = 100

#TODO: the saving is kind of messy. the logger needs to be reviewed

log_dir = './testing/'

# Train the agent every X steps
train_every = 1

agent = agents.ddqn.DQNAgent(action_num=eval_env.action_num) 
#agent = agents.actor_critic.Actor_Critic(action_num=eval_env.action_num)

random_agent = random_agent.RandomAgent(action_num=eval_env.action_num)
rule_based_agent = simpledoudizhu_rule_models.DouDizhuRuleAgentV1()

#agent.actor = keras.models.load_model('models/a2c/actor_d1.h5')
#agent.critic = keras.models.load_model('models/a2c/critic_d1.h5')

#agent.q_estimator = keras.models.load_model('models/dqn/q_estimator_a1.h5')
#agent.target_estimator = keras.models.load_model('models/dqn/target_estimator_a1.h5')

env.set_agents([agent, rule_based_agent, random_agent])
#eval_env.set_agents([agent, rule_based_agent, rule_based_agent])

logger = logger.Logger(log_dir)

start_time= time.time()
logger.log_performance(1, tournament(env, evaluate_num)[0])
print(start_time - time.time())