66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
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# AI for Breakout
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# Importing the librairies
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# Initializing and setting the variance of a tensor of weights
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def normalized_columns_initializer(weights, std=1.0):
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out = torch.randn(weights.size())
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out *= std / torch.sqrt(out.pow(2).sum(1, True))
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return out
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# Initializing the weights of the neural network in an optimal way for the learning
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def weights_init(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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weight_shape = list(m.weight.data.size())
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fan_in = np.prod(weight_shape[1:4])
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fan_out = np.prod(weight_shape[2:4]) * weight_shape[0]
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w_bound = np.sqrt(6. / (fan_in + fan_out))
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m.weight.data.uniform_(-w_bound, w_bound)
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m.bias.data.fill_(0)
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elif classname.find('Linear') != -1:
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weight_shape = list(m.weight.data.size())
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fan_in = weight_shape[1]
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fan_out = weight_shape[0]
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w_bound = np.sqrt(6. / (fan_in + fan_out))
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m.weight.data.uniform_(-w_bound, w_bound)
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m.bias.data.fill_(0)
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# Making the A3C brain
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class ActorCritic(torch.nn.Module):
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def __init__(self, num_inputs, action_space):
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super(ActorCritic, self).__init__()
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self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1)
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self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.lstm = nn.LSTMCell(32 * 3 * 3, 256)
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num_outputs = action_space.n
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self.critic_linear = nn.Linear(256, 1)
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self.actor_linear = nn.Linear(256, num_outputs)
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self.apply(weights_init)
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self.actor_linear.weight.data = normalized_columns_initializer(self.actor_linear.weight.data, 0.01)
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self.actor_linear.bias.data.fill_(0)
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self.critic_linear.weight.data = normalized_columns_initializer(self.critic_linear.weight.data, 1.0)
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self.critic_linear.bias.data.fill_(0)
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self.lstm.bias_ih.data.fill_(0)
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self.lstm.bias_hh.data.fill_(0)
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self.train()
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def forward(self, inputs):
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inputs, (hx, cx) = inputs
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x = F.elu(self.conv1(inputs))
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x = F.elu(self.conv2(x))
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x = F.elu(self.conv3(x))
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x = F.elu(self.conv4(x))
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x = x.view(-1, 32 * 3 * 3)
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hx, cx = self.lstm(x, (hx, cx))
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x = hx
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return self.critic_linear(x), self.actor_linear(x), (hx, cx)
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