.

src/agent/model.py

+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class QNetwork(nn.Module):
+    def __init__(self, state_size, action_size, seed, hidsize1=64, hidsize2=128):
+        super(QNetwork, self).__init__()
+
+        self.fc1_val = nn.Linear(state_size, hidsize1)
+        self.fc2_val = nn.Linear(hidsize1, hidsize2)
+        self.fc3_val = nn.Linear(hidsize2, 1)
+
+        self.fc1_adv = nn.Linear(state_size, hidsize1)
+        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
+        self.fc3_adv = nn.Linear(hidsize2, action_size)
+
+    def forward(self, x):
+        val = F.relu(self.fc1_val(x))
+        val = F.relu(self.fc2_val(val))
+        val = self.fc3_val(val)
+
+        # advantage calculation
+        adv = F.relu(self.fc1_adv(x))
+        adv = F.relu(self.fc2_adv(adv))
+        adv = self.fc3_adv(adv)
+        return val + adv - adv.mean()
+
+
+class QNetwork2(nn.Module):
+    def __init__(self, state_size, action_size, seed, input_channels, hidsize1=128, hidsize2=64):
+        super(QNetwork2, self).__init__()
+        self.conv1 = nn.Conv2d(input_channels, 16, kernel_size=3, stride=1)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=3)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=5, stride=3)
+        self.bn3 = nn.BatchNorm2d(64)
+
+        self.fc1_val = nn.Linear(6400, hidsize1)
+        self.fc2_val = nn.Linear(hidsize1, hidsize2)
+        self.fc3_val = nn.Linear(hidsize2, 1)
+
+        self.fc1_adv = nn.Linear(6400, hidsize1)
+        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
+        self.fc3_adv = nn.Linear(hidsize2, action_size)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+
+        # value function approximation
+        val = F.relu(self.fc1_val(x.view(x.size(0), -1)))
+        val = F.relu(self.fc2_val(val))
+        val = self.fc3_val(val)
+
+        # advantage calculation
+        adv = F.relu(self.fc1_adv(x.view(x.size(0), -1)))
+        adv = F.relu(self.fc2_adv(adv))
+        adv = self.fc3_adv(adv)
+        return val + adv - adv.mean()