HPCC2025/Duel_Double_DQN/utils.py

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import json
from datetime import datetime
import copy
def evaluate_policy(env, agent, turns=3):
"""
评估策略
Args:
env: 环境对象
agent: 智能体对象
turns: 评估轮数
Returns:
int: 平均得分
"""
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total_scores = 0
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# for j in range(turns):
s = env.reset()
done = False
eval_info = {'action_series': [],
# 'state_series': [],
'reward_series': []}
info_lt = []
while not done:
a = agent.select_action(s, deterministic=True)
s_next, r, dw, tr, info = env.step(a)
done = (dw or tr)
eval_info['action_series'].append(a)
eval_info['reward_series'].append(r)
info_lt.append(copy.deepcopy(info))
total_scores += r
s = s_next
print(eval_info)
save_best_solution(info_lt)
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return int(total_scores/turns)
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def save_best_solution(info_lt):
# 找出这一轮中最优的解
best_info = min(info_lt, key=lambda x: x['best_time'])
# 读取已有的最优解
try:
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with open('solutions/dqn_params_50_50_3.json', 'r') as f:
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saved_solution = json.load(f)
saved_time = saved_solution['best_time']
except FileNotFoundError:
saved_time = float('inf')
# 如果新的解更好则更新json文件
if best_info['best_time'] < saved_time:
best_solution = {
'best_time': best_info['best_time'],
'row_cuts': best_info['row_cuts'],
'col_cuts': best_info['col_cuts'],
'best_path': best_info['best_path'],
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
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with open('solutions/dqn_params_100_100_6.json', 'w') as f:
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json.dump(best_solution, f, indent=4)
print(f"发现新的最优解!时间: {best_info['best_time']}")
def compare_lists(list1, list2):
return len(list1) == len(list2) and all(a == b for a, b in zip(list1, list2))
# You can just ignore this funciton. Is not related to the RL.
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def str2bool(v):
'''transfer str to bool for argparse'''
if isinstance(v, bool):
return v
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if v.lower() in ('yes', 'True', 'true', 'TRUE', 't', 'y', '1'):
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return True
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elif v.lower() in ('no', 'False', 'false', 'FALSE', 'f', 'n', '0'):
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return False
else:
print('Wrong Input.')
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raise