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update PolicyGradient

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JohnJim0816
2020-11-27 18:34:04 +08:00
parent 9590e80a2b
commit abfe6ea62b
38 changed files with 210 additions and 22 deletions
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75
codes/PolicyGradient/main.py
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@@ -5,28 +5,38 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2020-11-23 12:06:15
LastEditTime: 2020-11-24 19:52:40
Discription:
Environment:
'''
from itertools import count
import torch
import os
from torch.utils.tensorboard import SummaryWriter
from env import env_init
from params import get_args
from agent import PolicyGradient
from params import SEQUENCE, SAVED_MODEL_PATH, RESULT_PATH
from utils import save_results,save_model
from plot import plot
def train(cfg):
env,n_states,n_actions = env_init()
env,state_dim,n_actions = env_init()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
agent = PolicyGradient(n_states,device = device,lr = cfg.policy_lr)
agent = PolicyGradient(state_dim,device = device,lr = cfg.policy_lr)
'''下面带pool都是存放的transition序列用于gradient'''
state_pool = [] # 存放每batch_size个episode的state序列
action_pool = []
reward_pool = []
''' 存储每个episode的reward用于绘图'''
rewards = []
moving_average_rewards = []
log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
writer = SummaryWriter(log_dir) # 使用tensorboard的writer
for i_episode in range(cfg.train_eps):
state = env.reset()
ep_reward = 0
for t in count():
for _ in count():
action = agent.choose_action(state) # 根据当前环境state选择action
next_state, reward, done, _ = env.step(action)
ep_reward += reward
@@ -39,14 +49,61 @@ def train(cfg):
if done:
print('Episode:', i_episode, ' Reward:', ep_reward)
break
# if i_episode % cfg.batch_size == 0:
if i_episode > 0 and i_episode % 5 == 0:
if i_episode > 0 and i_episode % cfg.batch_size == 0:
agent.update(reward_pool,state_pool,action_pool)
state_pool = [] # 每个episode的state
action_pool = []
reward_pool = []
rewards.append(ep_reward)
if i_episode == 0:
moving_average_rewards.append(ep_reward)
else:
moving_average_rewards.append(
0.9*moving_average_rewards[-1]+0.1*ep_reward)
writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode+1)
writer.close()
print('Complete training！')
save_model(agent,model_path=SAVED_MODEL_PATH)
'''存储reward等相关结果'''
save_results(rewards,moving_average_rewards,tag='train',result_path=RESULT_PATH)
plot(rewards)
plot(moving_average_rewards,ylabel='moving_average_rewards_train')
def eval(cfg,saved_model_path = SAVED_MODEL_PATH):
env,state_dim,n_actions = env_init()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
agent = PolicyGradient(state_dim,device = device,lr = cfg.policy_lr)
agent.load_model(saved_model_path+'checkpoint.pth')
rewards = []
moving_average_rewards = []
log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
writer = SummaryWriter(log_dir) # 使用tensorboard的writer
for i_episode in range(cfg.eval_eps):
state = env.reset()
ep_reward = 0
for _ in count():
action = agent.choose_action(state) # 根据当前环境state选择action
next_state, reward, done, _ = env.step(action)
ep_reward += reward
state = next_state
if done:
print('Episode:', i_episode, ' Reward:', ep_reward)
break
rewards.append(ep_reward)
if i_episode == 0:
moving_average_rewards.append(ep_reward)
else:
moving_average_rewards.append(
0.9*moving_average_rewards[-1]+0.1*ep_reward)
writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode+1)
writer.close()
print('Complete evaling！')
if __name__ == "__main__":
cfg = get_args()
train(cfg)
if cfg.train:
train(cfg)
eval(cfg)
else:
model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
eval(cfg,saved_model_path=model_path)