#!/usr/bin/env python
# coding=utf-8
'''
@Author: John
@Email: johnjim0816@gmail.com
@Date: 2020-06-09 20:25:52
@LastEditor: John
LastEditTime: 2022-09-27 15:43:21
@Discription: 
@Environment: python 3.7.7
'''
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

class DDPG:
    def __init__(self, models,memories,cfg):
        self.device = torch.device(cfg['device'])
        self.critic = models['critic'].to(self.device)
        self.target_critic = models['critic'].to(self.device)
        self.actor = models['actor'].to(self.device)
        self.target_actor = models['actor'].to(self.device)
        # copy weights from critic to target_critic
        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
            target_param.data.copy_(param.data)
        # copy weights from actor to target_actor
        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
            target_param.data.copy_(param.data)
        self.critic_optimizer = optim.Adam(self.critic.parameters(),  lr=cfg['critic_lr'])
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg['actor_lr'])
        self.memory = memories['memory']
        self.batch_size = cfg['batch_size']
        self.gamma = cfg['gamma']
        self.tau = cfg['tau']

    def sample_action(self, state):
        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        action = self.actor(state)
        return action.detach().cpu().numpy()[0, 0]
    @torch.no_grad()
    def predict_action(self, state):
        ''' predict action
        '''
        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        action = self.actor(state)
        return action.cpu().numpy()[0, 0]

    def update(self):
        if len(self.memory) < self.batch_size: # when memory size is less than batch size, return
            return
        # sample a random minibatch of N transitions from R
        state, action, reward, next_state, done = self.memory.sample(self.batch_size)
        # convert to tensor
        state = torch.FloatTensor(np.array(state)).to(self.device)
        next_state = torch.FloatTensor(np.array(next_state)).to(self.device)
        action = torch.FloatTensor(np.array(action)).to(self.device)
        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
        done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
       
        policy_loss = self.critic(state, self.actor(state))
        policy_loss = -policy_loss.mean()
        next_action = self.target_actor(next_state)
        target_value = self.target_critic(next_state, next_action.detach())
        expected_value = reward + (1.0 - done) * self.gamma * target_value
        expected_value = torch.clamp(expected_value, -np.inf, np.inf)

        value = self.critic(state, action)
        value_loss = nn.MSELoss()(value, expected_value.detach())
        
        self.actor_optimizer.zero_grad()
        policy_loss.backward()
        self.actor_optimizer.step()
        self.critic_optimizer.zero_grad()
        value_loss.backward()
        self.critic_optimizer.step()
        # soft update
        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
            target_param.data.copy_(
                target_param.data * (1.0 - self.tau) +
                param.data * self.tau
            )
        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
            target_param.data.copy_(
                target_param.data * (1.0 - self.tau) +
                param.data * self.tau
            )
    def save_model(self,path):
        from pathlib import Path
        # create path
        Path(path).mkdir(parents=True, exist_ok=True)
        torch.save(self.actor.state_dict(), f"{path}/actor_checkpoint.pt")

    def load_model(self,path):
        self.actor.load_state_dict(torch.load(f"{path}/actor_checkpoint.pt"))