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update codes

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johnjim0816
2021-11-17 14:36:51 +08:00
parent 8e5090a653
commit 442e307b01
81 changed files with 976 additions and 401 deletions
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36
codes/PolicyGradient/task0_train.py
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@@ -5,14 +5,14 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2021-05-05 17:35:20
LastEditTime: 2021-10-16 00:34:13
Discription:
Environment:
'''
import sys,os
curr_path = os.path.dirname(__file__)
parent_path = os.path.dirname(curr_path)
sys.path.append(parent_path) # add current terminal path to sys.path
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
parent_path = os.path.dirname(curr_path) # 父路径
sys.path.append(parent_path) # 添加父路径到系统路径sys.path
import gym
import torch
@@ -23,21 +23,20 @@ from PolicyGradient.agent import PolicyGradient
from common.plot import plot_rewards
from common.utils import save_results,make_dir
curr_time = datetime.datetime.now().strftime(
"%Y%m%d-%H%M%S") # obtain current time
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
class PGConfig:
def __init__(self):
self.algo = "PolicyGradient" # name of algo
self.env = 'CartPole-v0'
self.algo = "PolicyGradient" # 算法名称
self.env = 'CartPole-v0' # 环境名称
self.result_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/results/' # path to save results
'/'+curr_time+'/results/' # 保存结果的路径
self.model_path = curr_path+"/outputs/" + self.env + \
'/'+curr_time+'/models/' # path to save models
self.train_eps = 300 # 训练的episode数目
self.eval_eps = 50
'/'+curr_time+'/models/' # 保存模型的路径
self.train_eps = 300 # 训练的回合数
self.eval_eps = 30 # 测试的回合数
self.batch_size = 8
self.lr = 0.01 # learning rate
self.lr = 0.01 # 学习率
self.gamma = 0.99
self.hidden_dim = 36 # dimmension of hidden layer
self.device = torch.device(
@@ -59,7 +58,7 @@ def train(cfg,env,agent):
reward_pool = []
rewards = []
ma_rewards = []
for i_episode in range(cfg.train_eps):
for i_ep in range(cfg.train_eps):
state = env.reset()
ep_reward = 0
for _ in count():
@@ -73,9 +72,9 @@ def train(cfg,env,agent):
reward_pool.append(reward)
state = next_state
if done:
print('Episode:', i_episode, ' Reward:', ep_reward)
print('Episode:', i_ep, ' Reward:', ep_reward)
break
if i_episode > 0 and i_episode % cfg.batch_size == 0:
if i_ep > 0 and i_ep % cfg.batch_size == 0:
agent.update(reward_pool,state_pool,action_pool)
state_pool = [] # 每个episode的state
action_pool = []
@@ -95,7 +94,7 @@ def eval(cfg,env,agent):
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
rewards = []
ma_rewards = []
for i_episode in range(cfg.eval_eps):
for i_ep in range(cfg.eval_eps):
state = env.reset()
ep_reward = 0
for _ in count():
@@ -106,7 +105,7 @@ def eval(cfg,env,agent):
reward = 0
state = next_state
if done:
print('Episode:', i_episode, ' Reward:', ep_reward)
print('Episode:', i_ep, ' Reward:', ep_reward)
break
rewards.append(ep_reward)
if ma_rewards:
@@ -116,6 +115,7 @@ def eval(cfg,env,agent):
ma_rewards.append(ep_reward)
print('complete evaling！')
return rewards, ma_rewards
if __name__ == "__main__":
cfg = PGConfig()