text: # -*- coding: utf-8 -*-
  # MIT License
  #
  # Copyright (c) 2017 Kai Arulkumaran
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
  # ==============================================================================
  from __future__ import division
  import math
  import torch
  from torch import nn
  from torch.nn import functional as F
  
  
  # Factorised NoisyLinear layer with bias
  class NoisyLinear(nn.Module):
    def __init__(self, in_features, out_features, std_init=0.5):
      super(NoisyLinear, self).__init__()
      self.module_name = 'noisy_linear'
      self.in_features = in_features
      self.out_features = out_features
      self.std_init = std_init
      self.weight_mu = nn.Parameter(torch.empty(out_features, in_features))
      self.weight_sigma = nn.Parameter(torch.empty(out_features, in_features))
      self.register_buffer('weight_epsilon', torch.empty(out_features, in_features))
      self.bias_mu = nn.Parameter(torch.empty(out_features))
      self.bias_sigma = nn.Parameter(torch.empty(out_features))
      self.register_buffer('bias_epsilon', torch.empty(out_features))
      self.reset_parameters()
      self.reset_noise()
  
    def reset_parameters(self):
      mu_range = 1 / math.sqrt(self.in_features)
      self.weight_mu.data.uniform_(-mu_range, mu_range)
      self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.in_features))
      self.bias_mu.data.uniform_(-mu_range, mu_range)
      self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.out_features))
  
    def _scale_noise(self, size):
      x = torch.randn(size)
      return x.sign().mul_(x.abs().sqrt_())
  
    def reset_noise(self):
      epsilon_in = self._scale_noise(self.in_features)
      epsilon_out = self._scale_noise(self.out_features)
      self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in))
      self.bias_epsilon.copy_(epsilon_out)
  
    def forward(self, input):
      if self.training:
        return F.linear(input, self.weight_mu + self.weight_sigma * self.weight_epsilon, self.bias_mu + self.bias_sigma * self.bias_epsilon)
      else:
        return F.linear(input, self.weight_mu, self.bias_mu)
  
  
  class DQN(nn.Module):
    def __init__(self, args, action_space):
      super(DQN, self).__init__()
      self.atoms = args.atoms
      self.action_space = action_space
  
      if args.architecture == 'canonical':
        self.convs = nn.Sequential(nn.Conv2d(args.history_length, 32, 8, stride=4, padding=0), nn.ReLU(),
                                   nn.Conv2d(32, 64, 4, stride=2, padding=0), nn.ReLU(),
                                   nn.Conv2d(64, 64, 3, stride=1, padding=0), nn.ReLU())
        self.conv_output_size = 3136
      elif args.architecture == 'data-efficient':
        self.convs = nn.Sequential(nn.Conv2d(args.history_length, 32, 5, stride=5, padding=0), nn.ReLU(),
                                   nn.Conv2d(32, 64, 5, stride=5, padding=0), nn.ReLU())
        self.conv_output_size = 576
      self.fc_h_v = NoisyLinear(self.conv_output_size, args.hidden_size, std_init=args.noisy_std)
      self.fc_h_a = NoisyLinear(self.conv_output_size, args.hidden_size, std_init=args.noisy_std)
      self.fc_z_v = NoisyLinear(args.hidden_size, self.atoms, std_init=args.noisy_std)
      self.fc_z_a = NoisyLinear(args.hidden_size, action_space * self.atoms, std_init=args.noisy_std)
  
      self.W_h = nn.Parameter(torch.rand(self.conv_output_size, args.hidden_size))
      self.W_c = nn.Parameter(torch.rand(args.hidden_size, 128))
      self.b_h = nn.Parameter(torch.zeros(args.hidden_size))
      self.b_c = nn.Parameter(torch.zeros(128))
      self.W = nn.Parameter(torch.rand(128, 128))
  
      self.W_p_1 = nn.Parameter(torch.rand(128, 64))
      self.b_p_1 = nn .Parameter(torch.zeros(64))
  
      self.W_p_2 = nn.Parameter(torch.rand(64, 128))
      self.b_p_2 = nn .Parameter(torch.zeros(128))
  
      self.state_rep = nn.Sequential(
          nn.Linear(self.conv_output_size, args.hidden_size),
          nn.LayerNorm(args.hidden_size),
          nn.ReLU(),
          nn.Linear(args.hidden_size, 128),
          nn.LayerNorm(128),
          nn.ReLU()
      )
      
      self.predictor = nn.Sequential(
          nn.Linear(128, 128),
          nn.ReLU(),
          nn.Linear(128, 128),
          nn.ReLU()
      )
      
      
  
    def forward(self, x, log=False):
      x = self.convs(x)
      x = x.view(-1, self.conv_output_size)
  
      v = self.fc_z_v(F.relu(self.fc_h_v(x)))  # Value stream
      a = self.fc_z_a(F.relu(self.fc_h_a(x)))  # Advantage stream
  
      v, a = v.view(-1, 1, self.atoms), a.view(-1, self.action_space, self.atoms)
      q = v + a - a.mean(1, keepdim=True)  # Combine streams
      if log:  # Use log softmax for numerical stability
        q = F.log_softmax(q, dim=2)  # Log probabilities with action over second dimension
      else:
        q = F.softmax(q, dim=2)  # Probabilities with action over second dimension
      
      # h = torch.matmul(x, self.W_h) + self.b_h # Contrastive head
      # h = nn.LayerNorm(h.shape[1])(h)
      # h = F.relu(h)
      # h = torch.matmul(h, self.W_c) + self.b_c # Contrastive head
      # h = nn.LayerNorm(128)(h)
      
      h = self.state_rep(x)
  
      # p = torch.matmul(h, self.W_p_1) + self.b_p_1
      # p = F.relu(p)
      # p = torch.matmul(p, self.W_p_2) + self.b_p_2
      # p = nn.LayerNorm(128)(p)
  
      p = self.predictor(h)
  
      return q, h, p
  
    def reset_noise(self):
      for name, module in self.named_children():
        if 'fc' in name:
          module.reset_noise()
  