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johnjim0816
2022-07-13 23:52:05 +08:00
parent 45cc4aff58
commit bab7f6fe8c
66 changed files with 247 additions and 841 deletions
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codes/DDPG/task0.py
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@@ -5,59 +5,51 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-11 20:58:21
@LastEditor: John
LastEditTime: 2022-06-09 19:05:20
LastEditTime: 2022-07-13 22:53:11
@Discription:
@Environment: python 3.7.7
'''
import sys,os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
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) # 添加路径到系统路径sys.path
curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
parent_path = os.path.dirname(curr_path) # parent path
sys.path.append(parent_path) # add to system path
import datetime
import gym
import torch
import argparse
from env import NormalizedActions,OUNoise
from ddpg import DDPG
from common.utils import save_results,make_dir
from common.utils import plot_rewards
from common.utils import plot_rewards,save_args
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
class Config:
'''超参数
'''
def __init__(self):
################################## 环境超参数 ###################################
self.algo_name = 'DDPG' # 算法名称
self.env_name = 'Pendulum-v1' # 环境名称，gym新版本（约0.21.0之后）中Pendulum-v0改为Pendulum-v1
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # 检测GPUgjgjlkhfsf风刀霜的撒发十
self.seed = 10 # 随机种子，置0则不设置随机种子
self.train_eps = 300 # 训练的回合数
self.test_eps = 20 # 测试的回合数
################################################################################
################################## 算法超参数 ###################################
self.gamma = 0.99 # 折扣因子
self.critic_lr = 1e-3 # 评论家网络的学习率
self.actor_lr = 1e-4 # 演员网络的学习率
self.memory_capacity = 8000 # 经验回放的容量
self.batch_size = 128 # mini-batch SGD中的批量大小
self.target_update = 2 # 目标网络的更新频率
self.hidden_dim = 256 # 网络隐藏层维度
self.soft_tau = 1e-2 # 软更新参数
################################################################################
################################# 保存结果相关参数 ################################
self.result_path = curr_path + "/outputs/" + self.env_name + \
'/' + curr_time + '/results/' # 保存结果的路径
self.model_path = curr_path + "/outputs/" + self.env_name + \
'/' + curr_time + '/models/' # 保存模型的路径
self.save = True # 是否保存图片
################################################################################
def get_args():
""" Hyperparameters
"""
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time
parser = argparse.ArgumentParser(description="hyperparameters")
parser.add_argument('--algo_name',default='DDPG',type=str,help="name of algorithm")
parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment")
parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity")
parser.add_argument('--batch_size',default=128,type=int)
parser.add_argument('--target_update',default=2,type=int)
parser.add_argument('--soft_tau',default=1e-2,type=float)
parser.add_argument('--hidden_dim',default=256,type=int)
parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
'/' + curr_time + '/results/' )
parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
'/' + curr_time + '/models/' ) # path to save models
parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
args = parser.parse_args()
args.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # check GPU
return args
def env_agent_config(cfg,seed=1):
env = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
@@ -67,9 +59,9 @@ def env_agent_config(cfg,seed=1):
agent = DDPG(n_states,n_actions,cfg)
return env,agent
def train(cfg, env, agent):
print('开始训练！')
print(f'环境：{cfg.env_name}，算法：{cfg.algo_name}，设备：{cfg.device}')
ou_noise = OUNoise(env.action_space) # 动作噪声
print('Start training!')
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
ou_noise = OUNoise(env.action_space) # noise of action
rewards = [] # 记录所有回合的奖励
ma_rewards = [] # 记录所有回合的滑动平均奖励
for i_ep in range(cfg.train_eps):
@@ -88,18 +80,18 @@ def train(cfg, env, agent):
agent.update()
state = next_state
if (i_ep+1)%10 == 0:
print('回合：{}/{}，奖励：{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward))
print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
rewards.append(ep_reward)
if ma_rewards:
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
else:
ma_rewards.append(ep_reward)
print('完成训练！')
print('Finish training!')
return rewards, ma_rewards
def test(cfg, env, agent):
print('开始测试！')
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
print('Start testing')
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
rewards = [] # 记录所有回合的奖励
ma_rewards = [] # 记录所有回合的滑动平均奖励
for i_ep in range(cfg.test_eps):
@@ -113,25 +105,25 @@ def test(cfg, env, agent):
next_state, reward, done, _ = env.step(action)
ep_reward += reward
state = next_state
print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))
rewards.append(ep_reward)
if ma_rewards:
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
else:
ma_rewards.append(ep_reward)
print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
print('完成测试！')
print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
print('Finish testing!')
return rewards, ma_rewards
if __name__ == "__main__":
cfg = Config()
# 训练
cfg = get_args()
# training
env,agent = env_agent_config(cfg,seed=1)
rewards, ma_rewards = train(cfg, env, agent)
make_dir(cfg.result_path, cfg.model_path)
save_args(cfg)
agent.save(path=cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
# 测试
# testing
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
rewards,ma_rewards = test(cfg,env,agent)