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aut6620 avatar aut6620 commented on May 19, 2024 1
        V_loss = self.value_criterion(excepted_value, next_value.detach()).mean()  # J_V

        # Dual Q net
        Q1_loss = self.Q1_criterion(excepted_Q1.float(), next_q_value.detach()**.float()**).mean() # J_Q

        # Q1_loss = Q1_loss.folat()

        Q2_loss = self.Q2_criterion(excepted_Q2.float(), next_q_value.detach().float()).mean()
        # Q2_loss = Q2_loss.float()

        pi_loss = (log_prob.float() - excepted_new_Q.float()).mean() # according to original paper

image

from deep-reinforcement-learning-with-pytorch.

aut6620 avatar aut6620 commented on May 19, 2024 1

1、change all the dtype to float
2、then i met the next bug,the picture is what i had done

from deep-reinforcement-learning-with-pytorch.

zhaoyanghandd avatar zhaoyanghandd commented on May 19, 2024

How to deal with it?

from deep-reinforcement-learning-with-pytorch.

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