update codes

codes/TD3/task1_train.py

@@ -1,41 +1,47 @@
 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 torch
 import gym
 import numpy as np
 import datetime
 
-
 from TD3.agent import TD3
 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 TD3Config:
 	def __init__(self) -> None:
-		self.algo = 'TD3'
-		self.env = 'Pendulum-v0'
-		self.seed = 0
-		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.algo = 'TD3' # 算法名称
+		self.env_name = 'Pendulum-v1' # 环境名称
+		self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+		self.train_eps = 600 # 训练的回合数
 		self.start_timestep = 25e3 # Time steps initial random policy is used
-		self.start_ep = 50 # Episodes initial random policy is used
+		self.epsilon_start = 50 # Episodes initial random policy is used
 		self.eval_freq = 10 # How often (episodes) we evaluate
-		self.train_eps = 600
 		self.max_timestep = 100000 # Max time steps to run environment
 		self.expl_noise = 0.1 # Std of Gaussian exploration noise
 		self.batch_size = 256 # Batch size for both actor and critic
 		self.gamma = 0.9 # gamma factor
-		self.lr = 0.0005 # Target network update rate 
+		self.lr = 0.0005 # 学习率
 		self.policy_noise = 0.2 # Noise added to target policy during critic update
 		self.noise_clip = 0.3  # Range to clip target policy noise
 		self.policy_freq = 2 # Frequency of delayed policy updates
-		self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+class PlotConfig(TD3Config):
+	def __init__(self) -> None:
+		super().__init__()
+		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
+		
+    
 
 # Runs policy for X episodes and returns average reward
 # A fixed seed is used for the eval environment
@@ -57,8 +63,10 @@ def eval(env,agent, seed, eval_episodes=10):
 	return avg_reward
 
 def train(cfg,env,agent):
-	rewards = []
-	ma_rewards = [] # moveing average reward
+	print('开始训练!')
+	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(int(cfg.train_eps)):
 		ep_reward = 0
 		ep_timesteps = 0
@@ -66,7 +74,7 @@ def train(cfg,env,agent):
 		while not done:
 			ep_timesteps += 1
 			# Select action randomly or according to policy
-			if i_ep < cfg.start_ep:
+			if i_ep < cfg.epsilon_start:
 				action = env.action_space.sample()
 			else:
 				action = (
@@ -81,32 +89,34 @@ def train(cfg,env,agent):
 			state = next_state
 			ep_reward += reward
 			# Train agent after collecting sufficient data
-			if i_ep+1 >= cfg.start_ep:
+			if i_ep+1 >= cfg.epsilon_start:
 				agent.update()
-		print(f"Episode:{i_ep+1}/{cfg.train_eps}, Step:{ep_timesteps}, Reward:{ep_reward:.3f}")
+		if (i_ep+1)%10 == 0: 
+			print('回合：{}/{}, 奖励：{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward))
 		rewards.append(ep_reward)
-		# 计算滑动窗口的reward
 		if ma_rewards:
 			ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
 		else:
-			ma_rewards.append(ep_reward) 	
+			ma_rewards.append(ep_reward) 
+	print('完成训练！')	
 	return rewards, ma_rewards
 
 
 if __name__ == "__main__":
 	cfg  = TD3Config()
-	env = gym.make(cfg.env)
-	env.seed(cfg.seed) # Set seeds
-	torch.manual_seed(cfg.seed)
-	np.random.seed(cfg.seed)
+	plot_cfg = PlotConfig()
+	env = gym.make(cfg.env_name)
+	env.seed(1) # 随机种子
+	torch.manual_seed(1)
+	np.random.seed(1)
 	state_dim = env.observation_space.shape[0]
 	action_dim = env.action_space.shape[0] 
 	max_action = float(env.action_space.high[0])
 	agent = TD3(state_dim,action_dim,max_action,cfg)
 	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",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
+	make_dir(plot_cfg.result_path,plot_cfg.model_path)
+	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")