def forward(self, x):
'''The feedforward step'''
x = self.model_body(x)
state_value = self.v(x)
raw_advantages = self.adv(x)
out = math_util.calc_q_value_logits(state_value, raw_advantages)
return out
def forward(self, x):
'''The feedforward step'''
x = self.conv_model(x)
x = x.view(x.size(0), -1) # to (batch_size, -1)
if hasattr(self, 'fc_model'):
x = self.fc_model(x)
state_value = self.v(x)
raw_advantages = self.adv(x)
out = math_util.calc_q_value_logits(state_value, raw_advantages)
return out
def test_calc_q_value_logits():
state_value = torch.tensor([[1.], [2.], [3.]])
advantages = torch.tensor([[0., 1.], [1., 1.], [1., 0.]])
result = torch.tensor([[0.5, 1.5], [2.0, 2.0], [3.5, 2.5]])
out = math_util.calc_q_value_logits(state_value, advantages)
assert torch.allclose(out, result)
