89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
import json
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from datetime import datetime
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import copy
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def evaluate_policy(env, agent, turns=3):
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"""
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评估策略
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Args:
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env: 环境对象
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agent: 智能体对象
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turns: 评估轮数
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Returns:
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int: 平均得分
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"""
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total_scores = 0
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# for j in range(turns):
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s = env.reset()
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done = False
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eval_info = {'action_series': [],
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# 'state_series': [],
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'reward_series': []}
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info_lt = []
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while not done:
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a = agent.select_action(s, deterministic=True)
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s_next, r, dw, tr, info = env.step(a)
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done = (dw or tr)
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eval_info['action_series'].append(a)
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eval_info['reward_series'].append(r)
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info_lt.append(copy.deepcopy(info))
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total_scores += r
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s = s_next
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print(eval_info)
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save_best_solution(info_lt)
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return int(total_scores/turns)
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def save_best_solution(info_lt):
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# 找出这一轮中最优的解
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best_info = min(info_lt, key=lambda x: x['best_time'])
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# 读取已有的最优解
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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)
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saved_time = saved_solution['best_time']
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except FileNotFoundError:
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saved_time = float('inf')
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# 如果新的解更好，则更新json文件
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if best_info['best_time'] < saved_time:
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best_solution = {
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'best_time': best_info['best_time'],
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'row_cuts': best_info['row_cuts'],
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'col_cuts': best_info['col_cuts'],
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'best_path': best_info['best_path'],
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'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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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)
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print(f"发现新的最优解！时间: {best_info['best_time']}")
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def compare_lists(list1, list2):
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return len(list1) == len(list2) and all(a == b for a, b in zip(list1, list2))
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# You can just ignore this funciton. Is not related to the RL.
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def str2bool(v):
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'''transfer str to bool for argparse'''
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if isinstance(v, bool):
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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
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else:
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print('Wrong Input.')
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raise
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