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update codes

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johnjim0816
2021-11-18 15:41:27 +08:00
parent 442e307b01
commit 129c0c65fa
103 changed files with 1025 additions and 558 deletions
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codes/PPO/task0_train.py
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@@ -10,14 +10,13 @@ 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) # 添加路径到系统路径
import gym
import torch
import datetime
import tqdm
from PPO.agent import PPO
from common.plot import plot_rewards
from common.utils import save_results,make_dir
@@ -26,12 +25,12 @@ curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current t
class PPOConfig:
def __init__(self) -> None:
self.env = 'CartPole-v0'
self.algo = 'PPO'
self.result_path = curr_path+"/results/" +self.env+'/'+curr_time+'/results/' # path to save results
self.model_path = curr_path+"/results/" +self.env+'/'+curr_time+'/models/' # path to save models
self.train_eps = 200 # max training episodes
self.eval_eps = 50
self.algo = "DQN" # 算法名称
self.env_name = 'CartPole-v0' # 环境名称
self.continuous = False # 环境是否为连续动作
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
self.train_eps = 200 # 训练的回合数
self.eval_eps = 20 # 测试的回合数
self.batch_size = 5
self.gamma=0.99
self.n_epochs = 4
@@ -41,10 +40,20 @@ class PPOConfig:
self.policy_clip=0.2
self.hidden_dim = 256
self.update_fre = 20 # frequency of agent update
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
class PlotConfig:
def __init__(self) -> None:
self.algo = "DQN" # 算法名称
self.env_name = 'CartPole-v0' # 环境名称
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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 env_agent_config(cfg,seed=1):
env = gym.make(cfg.env)
env = gym.make(cfg.env_name)
env.seed(seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
@@ -53,44 +62,44 @@ def env_agent_config(cfg,seed=1):
def train(cfg,env,agent):
print('开始训练！')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
rewards= []
ma_rewards = [] # moving average rewards
running_steps = 0
print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{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
steps = 0
for i_ep in range(cfg.train_eps):
state = env.reset()
done = False
ep_reward = 0
while not done:
action, prob, val = agent.choose_action(state)
action, prob, val = agent.choose_action(state,continuous=cfg.continuous)
state_, reward, done, _ = env.step(action)
running_steps += 1
steps += 1
ep_reward += reward
agent.memory.push(state, action, prob, val, reward, done)
if running_steps % cfg.update_fre == 0:
if steps % cfg.update_fre == 0:
agent.update()
state = state_
rewards.append(ep_reward)
if ma_rewards:
ma_rewards.append(
0.9*ma_rewards[-1]+0.1*ep_reward)
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.train_eps}，奖励：{ep_reward:.2f}")
print('Complete training！')
if (i_ep+1)%10 == 0:
print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}")
print('完成训练！')
return rewards,ma_rewards
def eval(cfg,env,agent):
print('Start to eval !')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
rewards= []
ma_rewards = [] # moving average rewards
print('开始测试!')
print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{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.eval_eps):
state = env.reset()
done = False
ep_reward = 0
while not done:
action, prob, val = agent.choose_action(state)
action, prob, val = agent.choose_action(state,cfg.continuous)
state_, reward, done, _ = env.step(action)
ep_reward += reward
state = state_
@@ -100,23 +109,23 @@ def eval(cfg,env,agent):
0.9*ma_rewards[-1]+0.1*ep_reward)
else:
ma_rewards.append(ep_reward)
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, Reward:{ep_reward:.3f}")
print('Complete evaling！')
print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.eval_eps, ep_reward))
print('完成训练！')
return rewards,ma_rewards
if __name__ == '__main__':
cfg = PPOConfig()
# train
plot_cfg = PlotConfig()
# 训练
env,agent = env_agent_config(cfg,seed=1)
rewards, ma_rewards = train(cfg, env, agent)
make_dir(cfg.result_path, cfg.model_path)
agent.save(path=cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
plot_rewards(rewards, ma_rewards, tag="train",
algo=cfg.algo, path=cfg.result_path)
# eval
make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
agent.save(path=plot_cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)
plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")
# 测试
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
agent.load(path=plot_cfg.model_path)
rewards,ma_rewards = eval(cfg,env,agent)
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path)
plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval")