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JohnJim0816
2021-03-23 17:05:29 +08:00
parent bf0f2990cf
commit 5d8bf4802a
15 changed files with 117 additions and 74 deletions
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22
codes/PolicyGradient/main.py
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@@ -5,12 +5,12 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2021-03-13 11:50:32
LastEditTime: 2021-03-23 16:38:54
Discription:
Environment:
'''
import sys,os
sys.path.append(os.getcwd()) # 添加当前终端路径
sys.path.append(os.getcwd()) # add current terminal path to sys.path
from itertools import count
import datetime
import gym
@@ -18,25 +18,25 @@ from PolicyGradient.agent import PolicyGradient
from common.plot import plot_rewards
from common.utils import save_results
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # path to save model
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"):
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
if not os.path.exists(SAVED_MODEL_PATH):
os.mkdir(SAVED_MODEL_PATH)
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # path to save rewards
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"):
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
if not os.path.exists(RESULT_PATH):
os.mkdir(RESULT_PATH)
class PGConfig:
def __init__(self):
self.train_eps = 300 # 训练的episode数目
self.batch_size = 8
self.lr = 0.01 # 学习率
self.lr = 0.01 # learning rate
self.gamma = 0.99
self.hidden_dim = 36 # 隐藏层维度
self.hidden_dim = 36 # dimmension of hidden layer
def train(cfg,env,agent):
'''下面带pool都是存放的transition序列用于gradient'''