#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-12 21:14:12
LastEditor: John
LastEditTime: 2021-05-04 02:45:27
Discription: 
Environment: 
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical

class MLP(nn.Module):
    def __init__(self, input_dim,output_dim,hidden_dim=128):
        """ 初始化q网络，为全连接网络
            input_dim: 输入的feature即环境的state数目
            output_dim: 输出的action总个数
        """
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim) # 输入层
        self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
        self.fc3 = nn.Linear(hidden_dim, output_dim) # 输出层
        
    def forward(self, x):
        # 各层对应的激活函数
        x = F.relu(self.fc1(x)) 
        x = F.relu(self.fc2(x))
        return self.fc3(x)

class Critic(nn.Module):
    def __init__(self, n_obs, output_dim, hidden_size, init_w=3e-3):
        super(Critic, self).__init__()
        
        self.linear1 = nn.Linear(n_obs + output_dim, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, 1)
        # 随机初始化为较小的值
        self.linear3.weight.data.uniform_(-init_w, init_w)
        self.linear3.bias.data.uniform_(-init_w, init_w)
        
    def forward(self, state, action):
        # 按维数1拼接
        x = torch.cat([state, action], 1)
        x = F.relu(self.linear1(x))
        x = F.relu(self.linear2(x))
        x = self.linear3(x)
        return x

class Actor(nn.Module):
    def __init__(self, n_obs, output_dim, hidden_size, init_w=3e-3):
        super(Actor, self).__init__()  
        self.linear1 = nn.Linear(n_obs, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, output_dim)
        
        self.linear3.weight.data.uniform_(-init_w, init_w)
        self.linear3.bias.data.uniform_(-init_w, init_w)
        
    def forward(self, x):
        x = F.relu(self.linear1(x))
        x = F.relu(self.linear2(x))
        x = torch.tanh(self.linear3(x))
        return x

class ActorCritic(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim=256):
        super(ActorCritic, self).__init__()
        self.critic = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )
        
        self.actor = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim),
            nn.Softmax(dim=1),
        )
        
    def forward(self, x):
        value = self.critic(x)
        probs = self.actor(x)
        dist  = Categorical(probs)
        return dist, value