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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class QNetwork(nn.Module):
"""State-action function approximator."""
def __init__(self, state_size, action_size, seed):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
"""
super(QNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
# my code
self.fc1 = nn.Linear(state_size, 64)
#self.fc1.weight.data.normal_(0,0.1) # initialization
self.fc2 = nn.Linear(64, 64)
#self.fc2.weight.data.normal_(0,0.1) # initialization
self.out = nn.Linear(64, action_size)
#self.out.weight.data.normal_(0,0.1) # initialization
def forward(self, state):
"""Build a network that maps state -> action values."""
state = self.fc1(state)
state = F.relu(state)
state = self.fc2(state)
state = F.relu(state)
action_value = self.out(state)
return action_value