added individual eps-greedy for VDN

algorithms/common.py

@@ -71,6 +71,7 @@ def mlp_maker(dims, flatten=False, activation='elu', activation_last='identity')
     return nn.Sequential(OrderedDict(layers))
 
 
+
 class BaseDQN(nn.Module):
     def __init__(self, dims=[3*5*5, 64, 64, 9]):
         super(BaseDQN, self).__init__()
@@ -100,3 +101,20 @@ class BaseDDQN(BaseDQN):
         advantages = self.advantage_head(features)
         values = self.value_head(features)
         return values + (advantages - advantages.mean())
+
+
+class QTRANtestNet(nn.Module):
+    def __init__(self, backbone_dims=[3*5*5, 64, 64], q_head=[64, 9]):
+        super(QTRANtestNet, self).__init__()
+        self.backbone = mlp_maker(backbone_dims, flatten=True, activation_last='elu')
+        self.q_head = mlp_maker(q_head)
+
+    def forward(self, x):
+        features = self.backbone(x)
+        qs = self.q_head(features)
+        return qs, features
+
+    @torch.no_grad()
+    def act(self, x) -> np.ndarray:
+        action = self.forward(x)[0].max(-1)[1].numpy()
+        return action