Major refactor

network/shallow_network.py

@@ -0,0 +1,64 @@
+#######################################################################
+# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com)    #
+# Permission given to modify the code as long as you keep this        #
+# declaration at the top                                              #
+#######################################################################
+
+from network import *
+
+# Network for CartPole with value based methods
+class FCNet(nn.Module, VanillaNet):
+    def __init__(self, dims, optimizer_fn=None, gpu=True):
+        super(FCNet, self).__init__()
+        self.fc1 = nn.Linear(dims[0], dims[1])
+        self.fc2 = nn.Linear(dims[1], dims[2])
+        self.fc3 = nn.Linear(dims[2], dims[3])
+        self.criterion = nn.MSELoss()
+        BasicNet.__init__(self, optimizer_fn, gpu)
+
+    def forward(self, x):
+        x = self.to_torch_variable(x)
+        x = x.view(x.size(0), -1)
+        y = F.relu(self.fc1(x))
+        y = F.relu(self.fc2(y))
+        y = self.fc3(y)
+        return y
+
+# Network for CartPole with dueling architecture
+class DuelingFCNet(nn.Module, DuelingNet):
+    def __init__(self, dims, optimizer_fn=None, gpu=True):
+        super(DuelingFCNet, self).__init__()
+        self.fc1 = nn.Linear(dims[0], dims[1])
+        self.fc2 = nn.Linear(dims[1], dims[2])
+        self.fc_value = nn.Linear(dims[2], 1)
+        self.fc_advantage = nn.Linear(dims[2], dims[3])
+        self.criterion = nn.MSELoss()
+        BasicNet.__init__(self, optimizer_fn, gpu)
+
+    def forward(self, x):
+        x = self.to_torch_variable(x)
+        x = x.view(x.size(0), -1)
+        y = F.relu(self.fc1(x))
+        phi = F.relu(self.fc2(y))
+        return phi
+
+# Network for CartPole with actor critic
+class ActorCriticFCNet(nn.Module, ActorCriticNet):
+    def __init__(self, state_dim, action_dim):
+        super(ActorCriticFCNet, self).__init__()
+        hidden_size1 = 50
+        hidden_size2 = 200
+        self.fc1 = nn.Linear(state_dim, hidden_size1)
+        self.fc2 = nn.Linear(hidden_size1, hidden_size2)
+        self.fc_actor = nn.Linear(hidden_size2, action_dim)
+        self.fc_critic = nn.Linear(hidden_size2, 1)
+        BasicNet.__init__(self, None, False)
+
+    def forward(self, x, update_LSTM=True):
+        x = self.to_torch_variable(x)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        phi = self.fc2(x)
+        return phi
+
+