Hi, I’m struggling to make my deep learning network for A2C model.

My networks can’t be updated bcz of error displayed bottom image.

I can’t understand what is the cause of this problem.

Does anyone know about this problem in detail?

(I already check my Critic-network gradient check by torch.autograd.gradcheck. it returns ‘True’.)

This is my network

```
class Actor_network(nn.Module):
"""Actor"""
'''model = nn.Sequential()
model.add_module('fc1', nn.Linear(STATE_DIM, NODES))
model.add_module('relu', nn.ReLU())
model.add_module('fc2', nn.Linear(NODES, 2))
model.add_module('soft', nn.Softmax(dim=0))
return model'''
def __init__(self):
super(Actor_network, self).__init__()
self.fc1 = nn.Linear(STATE_DIM, NODES)
self.fc2 = nn.Linear(NODES, 2)
def Forward(self, x):
x = F.relu(self.fc1(x))
x = F.softmax(self.fc2(x), dim=0)
return x
class Critic_network(nn.Module):
"""Critic"""
'''model = nn.Sequential()
model.add_module('fc1', nn.Linear(STATE_DIM, NODES))
model.add_module('relu1', nn.ReLU())
model.add_module('fc2', nn.Linear(NODES, 1))
return model'''
def __init__(self):
super(Critic_network, self).__init__()
self.fc1 = nn.Linear(STATE_DIM, NODES)
self.fc2 = nn.Linear(NODES, 1)
def Forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
```