51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-12-22 15:22:17
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LastEditor: John
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LastEditTime: 2021-01-21 14:30:38
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Discription:
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Environment:
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'''
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import datetime
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import os
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import argparse
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ALGO_NAME = 'Double DQN'
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/'
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TRAIN_LOG_DIR=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
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EVAL_LOG_DIR=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
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def get_args():
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'''模型参数
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--train", default=1, type=int) # 1 表示训练，0表示只进行eval
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parser.add_argument("--gamma", default=0.99,
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type=float) # q-learning中的gamma
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parser.add_argument("--epsilon_start", default=0.95,
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type=float) # 基于贪心选择action对应的参数epsilon
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parser.add_argument("--epsilon_end", default=0.01, type=float)
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parser.add_argument("--epsilon_decay", default=500, type=float)
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parser.add_argument("--policy_lr", default=0.01, type=float)
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parser.add_argument("--memory_capacity", default=1000,
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type=int, help="capacity of Replay Memory")
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parser.add_argument("--batch_size", default=32, type=int,
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help="batch size of memory sampling")
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parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
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parser.add_argument("--train_steps", default=200, type=int)
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parser.add_argument("--target_update", default=2, type=int,
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help="when(every default 2 eisodes) to update target net ") # 更新频率
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parser.add_argument("--eval_eps", default=100, type=int) # 训练的最大episode数目
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parser.add_argument("--eval_steps", default=200,
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type=int) # 训练每个episode的长度
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config = parser.parse_args()
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return config |