147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
@Author: John
|
||
@Email: johnjim0816@gmail.com
|
||
@Date: 2020-06-11 20:58:21
|
||
@LastEditor: John
|
||
LastEditTime: 2020-09-02 01:24:50
|
||
@Discription:
|
||
@Environment: python 3.7.7
|
||
'''
|
||
import torch
|
||
import gym
|
||
|
||
from ddpg import DDPG
|
||
from env import NormalizedActions
|
||
from noise import OUNoise
|
||
from plot import plot
|
||
|
||
import argparse
|
||
|
||
def get_args():
|
||
'''模型建立好之后只需要在这里调参
|
||
'''
|
||
parser = argparse.ArgumentParser()
|
||
|
||
parser.add_argument("--gamma", default=0.99, type=float) # q-learning中的gamma
|
||
parser.add_argument("--critic_lr", default=1e-3, type=float) # critic学习率
|
||
parser.add_argument("--actor_lr", default=1e-4, type=float)
|
||
|
||
parser.add_argument("--memory_capacity", default=10000, type=int,help="capacity of Replay Memory")
|
||
|
||
parser.add_argument("--batch_size", default=128, type=int,help="batch size of memory sampling")
|
||
parser.add_argument("--train_eps", default=200, type=int)
|
||
parser.add_argument("--train_steps", default=200, type=int)
|
||
parser.add_argument("--eval_eps", default=200, type=int) # 训练的最大episode数目
|
||
parser.add_argument("--eval_steps", default=200, type=int) # 训练每个episode的长度
|
||
parser.add_argument("--target_update", default=4, type=int,help="when(every default 10 eisodes) to update target net ")
|
||
config = parser.parse_args()
|
||
return config
|
||
|
||
def train():
|
||
cfg = get_args()
|
||
env = NormalizedActions(gym.make("Pendulum-v0"))
|
||
|
||
# 增加action噪声
|
||
ou_noise = OUNoise(env.action_space)
|
||
|
||
n_states = env.observation_space.shape[0]
|
||
n_actions = env.action_space.shape[0]
|
||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
agent=DDPG(n_states,n_actions,device="cpu", critic_lr=1e-3,
|
||
actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
|
||
|
||
rewards = []
|
||
moving_average_rewards = []
|
||
ep_steps = []
|
||
for i_episode in range(1,cfg.train_eps+1):
|
||
state=env.reset()
|
||
ou_noise.reset()
|
||
ep_reward = 0
|
||
for i_step in range(1,cfg.train_steps+1):
|
||
action = agent.select_action(state)
|
||
action = ou_noise.get_action(action, i_step) # 即paper中的random process
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
agent.memory.push(state, action, reward, next_state, done)
|
||
agent.update()
|
||
state = next_state
|
||
if done:
|
||
break
|
||
print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),'n_steps:', i_step)
|
||
ep_steps.append(i_step)
|
||
rewards.append(ep_reward)
|
||
if i_episode == 1:
|
||
moving_average_rewards.append(ep_reward)
|
||
else:
|
||
moving_average_rewards.append(
|
||
0.9*moving_average_rewards[-1]+0.1*ep_reward)
|
||
print('Complete！')
|
||
# 保存模型
|
||
import os
|
||
import numpy as np
|
||
save_path = os.path.dirname(__file__)+"/saved_model/"
|
||
if not os.path.exists(save_path):
|
||
os.mkdir(save_path)
|
||
agent.save_model(save_path+'checkpoint.pth')
|
||
# 存储reward等相关结果
|
||
output_path = os.path.dirname(__file__)+"/result/"
|
||
# 检测是否存在文件夹
|
||
if not os.path.exists(output_path):
|
||
os.mkdir(output_path)
|
||
np.save(output_path+"rewards.npy", rewards)
|
||
np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
|
||
np.save(output_path+"steps.npy", ep_steps)
|
||
plot(rewards)
|
||
plot(moving_average_rewards,ylabel="moving_average_rewards")
|
||
plot(ep_steps, ylabel="steps_of_each_episode")
|
||
|
||
def eval():
|
||
cfg = get_args()
|
||
env = NormalizedActions(gym.make("Pendulum-v0"))
|
||
|
||
# 增加action噪声
|
||
ou_noise = OUNoise(env.action_space)
|
||
|
||
n_states = env.observation_space.shape[0]
|
||
n_actions = env.action_space.shape[0]
|
||
agent=DDPG(n_states,n_actions, critic_lr=1e-3,
|
||
actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
|
||
|
||
import os
|
||
save_path = os.path.dirname(__file__)+"/saved_model/"
|
||
if not os.path.exists(save_path):
|
||
os.mkdir(save_path)
|
||
agent.load_model(save_path+'checkpoint.pth')
|
||
rewards = []
|
||
moving_average_rewards = []
|
||
ep_steps = []
|
||
for i_episode in range(1, cfg.eval_eps+1):
|
||
state = env.reset() # reset环境状态
|
||
ep_reward = 0
|
||
for i_step in range(1, cfg.eval_steps+1):
|
||
action = agent.select_action(state) # 根据当前环境state选择action
|
||
next_state, reward, done, _ = env.step(action) # 更新环境参数
|
||
ep_reward += reward
|
||
state = next_state # 跳转到下一个状态
|
||
if done:
|
||
break
|
||
print('Episode:', i_episode, ' Reward: %i' %
|
||
int(ep_reward), 'n_steps:', i_step, 'done: ', done)
|
||
ep_steps.append(i_step)
|
||
rewards.append(ep_reward)
|
||
# 计算滑动窗口的reward
|
||
if i_episode == 1:
|
||
moving_average_rewards.append(ep_reward)
|
||
else:
|
||
moving_average_rewards.append(
|
||
0.9*moving_average_rewards[-1]+0.1*ep_reward)
|
||
plot(rewards,save_fig=False)
|
||
plot(moving_average_rewards, ylabel="moving_average_rewards",save_fig=False)
|
||
plot(ep_steps, ylabel="steps_of_each_episode",save_fig=False)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# train()
|
||
eval() |