residual_dqn.py 文件源码

def _compute_y_and_t(self, exp_batch, gamma):

        batch_state = exp_batch['state']
        batch_size = len(batch_state)

        # Compute Q-values for current states
        qout = self.q_function(batch_state)

        batch_actions = exp_batch['action']
        batch_q = F.reshape(qout.evaluate_actions(
            batch_actions), (batch_size, 1))

        # Target values must also backprop gradients
        batch_q_target = F.reshape(
            self._compute_target_values(exp_batch, gamma), (batch_size, 1))

        return batch_q, scale_grad.scale_grad(batch_q_target, self.grad_scale)