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 = '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.start_timestep = 25e3 # Time steps initial random policy is used
		self.start_ep = 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.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")

# 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):
	rewards = []
	ma_rewards = [] # moveing average reward
	for i_ep in range(int(cfg.train_eps)):
		ep_reward = 0
		ep_timesteps = 0
		state, done = env.reset(), False
		while not done:
			ep_timesteps += 1
			# Select action randomly or according to policy
			if i_ep < cfg.start_ep:
				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 i_ep+1 >= cfg.start_ep:
				agent.update()
		print(f"Episode:{i_ep+1}/{cfg.train_eps}, Step:{ep_timesteps}, 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) 	
	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)