2381a50a70559340a0335288d648b4bb9a675588,slm_lab/agent/algorithm/dqn.py,HydraDQN,train,#HydraDQN#,498

Before Change

                    losses = self.net.training_step(batch["states"], y)
                    logger.debug(f"losses {losses}")
                    batch_losses += losses.item()
                batch_losses /= self.training_iters_per_batch
                nanflat_loss_a += batch_losses
            nanflat_loss_a /= self.training_epoch
            loss_a = self.nanflat_to_data_a("loss", nanflat_loss_a)
            return loss_a
        else:
            logger.debug3("NOT training")
            return np.nan

After Change

        """
        total_t = util.s_get(self, "aeb_space.clock").get("total_t")
        self.to_train = (total_t > self.training_min_timestep and total_t % self.training_frequency == 0)
        if self.to_train == 1:
            total_loss = torch.tensor(0.0)
            for _ in range(self.training_epoch):
                batch = self.sample()
                with torch.no_grad():
                    q_targets = self.calc_q_targets(batch)
                loss = self.net.training_step(batch["states"], q_targets)
                total_loss += loss
            loss = total_loss / self.training_epoch
            // reset
            self.to_train = 0
            self.body.log_probs = []
            self.body.entropies = []
            logger.debug(f"Loss: {loss}")
            self.last_loss = loss.item()
        return self.last_loss

Instances