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codes/TD3/task0_train.py

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+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
+
+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
+	
+
+class TD3Config:
+	def __init__(self) -> None:
+		self.algo = 'TD3'
+		self.env = 'HalfCheetah-v2'
+		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.start_timestep = 25e3 # Time steps initial random policy is used
+		self.eval_freq = 5e3 # How often (time steps) we evaluate
+		# self.train_eps = 800
+		self.max_timestep = 4000000 # 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 = 0.99 # gamma factor
+		self.lr = 0.0005 # Target network update rate 
+		self.policy_noise = 0.2 # Noise added to target policy during critic update
+		self.noise_clip = 0.5  # 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")
+		
+# Runs policy for X episodes and returns average reward
+# A fixed seed is used for the eval environment
+def eval(env,agent, seed, eval_episodes=10):
+	eval_env = gym.make(env)
+	eval_env.seed(seed + 100)
+	avg_reward = 0.
+	for _ in range(eval_episodes):
+		state, done = eval_env.reset(), False
+		while not done:
+			# eval_env.render()
+			action = agent.choose_action(np.array(state))
+			state, reward, done, _ = eval_env.step(action)
+			avg_reward += reward
+	avg_reward /= eval_episodes
+	print("---------------------------------------")
+	print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
+	print("---------------------------------------")
+	return avg_reward
+
+def train(cfg,env,agent):
+	# Evaluate untrained policy
+	evaluations = [eval(cfg.env,agent, cfg.seed)]
+	state, done = env.reset(), False
+	ep_reward = 0
+	ep_timesteps = 0
+	episode_num = 0
+	rewards = []
+	ma_rewards = [] # moveing average reward
+	for t in range(int(cfg.max_timestep)):
+		ep_timesteps += 1
+		# Select action randomly or according to policy
+		if t < cfg.start_timestep:
+			action = env.action_space.sample()
+		else:
+			action = (
+				agent.choose_action(np.array(state))
+				+ np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
+			).clip(-max_action, max_action)
+		# Perform action
+		next_state, reward, done, _ = env.step(action) 
+		done_bool = float(done) if ep_timesteps < env._max_episode_steps else 0
+		# Store data in replay buffer
+		agent.memory.push(state, action, next_state, reward, done_bool)
+		state = next_state
+		ep_reward += reward
+		# Train agent after collecting sufficient data
+		if t >= cfg.start_timestep:
+			agent.update()
+		if done: 
+			# +1 to account for 0 indexing. +0 on ep_timesteps since it will increment +1 even if done=True
+			print(f"Episode:{episode_num+1}, Episode T:{ep_timesteps}, Reward:{ep_reward:.3f}")
+			# Reset environment
+			state, done = env.reset(), False
+			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) 
+			ep_reward = 0
+			ep_timesteps = 0
+			episode_num += 1 
+		# Evaluate episode
+		if (t + 1) % cfg.eval_freq == 0:
+			evaluations.append(eval(cfg.env,agent, cfg.seed))
+	return rewards, ma_rewards
+# def train(cfg,env,agent):
+# 	evaluations = [eval(cfg.env,agent,cfg.seed)]
+# 	ep_reward = 0
+# 	tot_timestep = 0
+# 	rewards = []
+# 	ma_rewards = [] # moveing average reward
+# 	for i_ep in range(int(cfg.train_eps)):
+# 		state, done = env.reset(), False
+# 		ep_reward = 0
+# 		ep_timestep = 0
+# 		while not done:
+# 			ep_timestep += 1
+# 			tot_timestep +=1
+# 			# Select action randomly or according to policy
+# 			if tot_timestep < cfg.start_timestep:
+# 				action = env.action_space.sample()
+# 			else:
+# 				action = (
+# 					agent.choose_action(np.array(state))
+# 					+ np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
+# 				).clip(-max_action, max_action)
+# 			# action = (
+# 			# 		agent.choose_action(np.array(state))
+# 			# 		+ np.random.normal(0, max_action * cfg.expl_noise, size=action_dim)
+# 			# 	).clip(-max_action, max_action)
+# 			# Perform action
+# 			next_state, reward, done, _ = env.step(action) 
+# 			done_bool = float(done) if ep_timestep < env._max_episode_steps else 0
+
+# 			# Store data in replay buffer
+# 			agent.memory.push(state, action, next_state, reward, done_bool)
+# 			state = next_state
+# 			ep_reward += reward
+# 			# Train agent after collecting sufficient data
+# 			if tot_timestep >= cfg.start_timestep:
+# 				agent.update()
+# 		print(f"Episode:{i_ep}/{cfg.train_eps}, Episode Timestep:{ep_timestep}, Reward:{ep_reward:.3f}")
+# 		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) 
+# 		# Evaluate episode
+# 		if (i_ep+1) % cfg.eval_freq == 0:
+# 			evaluations.append(eval(cfg.env,agent, cfg.seed))
+# 	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)
+	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)
+	# cfg.result_path = './TD3/results/HalfCheetah-v2/20210416-130341/'
+	# agent.load(cfg.result_path)
+	# eval(cfg.env,agent, cfg.seed)
+	
+