A3CModel.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


def norm_col_init(weights, std=1.0):
    x = torch.randn(weights.size())
    x *= std / torch.sqrt((x**2).sum(1, keepdim=True))
    return x

def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        weight_shape = list(m.weight.data.size())
        fan_in = np.prod(weight_shape[1:4])
        fan_out = np.prod(weight_shape[2:4]) * weight_shape[0]
        w_bound = np.sqrt(6. / (fan_in + fan_out))
        m.weight.data.uniform_(-w_bound, w_bound)
        m.bias.data.fill_(0)
    elif classname.find('Linear') != -1:
        weight_shape = list(m.weight.data.size())
        fan_in = weight_shape[1]
        fan_out = weight_shape[0]
        w_bound = np.sqrt(6. / (fan_in + fan_out))
        m.weight.data.uniform_(-w_bound, w_bound)
        m.bias.data.fill_(0)

class A3Clstm(torch.nn.Module):
    def __init__(self, num_inputs, action_space):
        super(A3Clstm, self).__init__()

        # convolutional neural networks
        self.conv1 = nn.Conv2d(num_inputs, 32, 5, stride=1, padding=2)
        self.maxp1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 32, 5, stride=1, padding=1)
        self.maxp2 = nn.MaxPool2d(2, 2)
        self.conv3 = nn.Conv2d(32, 64, 4, stride=1, padding=1)
        self.maxp3 = nn.MaxPool2d(2, 2)
        self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
        self.maxp4 = nn.MaxPool2d(2, 2)

        # LSTM Cells
        self.lstm = nn.LSTMCell(1024, 512)
        num_outputs = action_space.n

        # The critic layer
        self.critic_linear = nn.Linear(512, 1)
        # The actor layer
        self.actor_linear = nn.Linear(512, num_outputs)

        self.apply(weights_init)
        self.actor_linear.weight.data = norm_col_init(
            self.actor_linear.weight.data, 0.01)
        self.actor_linear.bias.data.fill_(0)
        self.critic_linear.weight.data = norm_col_init(
            self.critic_linear.weight.data, 1.0)
        self.critic_linear.bias.data.fill_(0)

        self.lstm.bias_ih.data.fill_(0)
        self.lstm.bias_hh.data.fill_(0)

        self.train()

    # forward propagation
    def forward(self, inputs):
        inputs, (hx, cx) = inputs
        x = F.relu(self.maxp1(self.conv1(inputs)))
        x = F.relu(self.maxp2(self.conv2(x)))
        x = F.relu(self.maxp3(self.conv3(x)))
        x = F.relu(self.maxp4(self.conv4(x)))

        x = x.view(x.size(0), -1)

        hx, cx = self.lstm(x, (hx, cx))

        x = hx

        return self.critic_linear(x), self.actor_linear(x), (hx, cx)