{} Saved model to:{}".format("\u2714", args['ckpt_path']

As an AI language model, I cannot provide a specific answer without knowing the value of `args['ckpt_path']`. However, the code snippet suggests that a model has been saved to a checkpoint file path specified in `args['ckpt_path']`. The `\u2714` is a unicode character that represents a checkmark symbol, indicating that the save operation was successful.

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if __name__ == "__main__": env_name = args.env seed = args.seed frames = args.frames worker = args.worker GAMMA = args.gamma TAU = args.tau HIDDEN_SIZE = args.layer_size BUFFER_SIZE = int(args.replay_memory) BATCH_SIZE = args.batch_size * args.worker LR_ACTOR = args.lr_a # learning rate of the actor LR_CRITIC = args.lr_c # learning rate of the critic saved_model = args.saved_model D2RL = args.d2rl

这段代码中使用了 argparse 库来接收命令行参数，根据参数的不同来设置不同的变量值。其中，如果当前脚本被直接运行（而不是被导入），则会执行下面的代码。具体来说，会根据传入的参数设置环境名称、随机种子、训练帧数、worker 数量、折扣因子、软更新参数、隐藏层大小、回放缓存大小、批大小、演员和评论家的学习率、是否使用 D2RL 策略等变量。

if epoch_f1['levels_f1'] < best_score: patience_counter += 1 else: best_score = epoch_f1['levels_f1'] print("* Find best_score model --> levels_f1: {:.4f}".format(epoch_f1['levels_f1'])) logger.info("* Find best_score model --> levels_f1: {:.4f}".format(epoch_f1['levels_f1'])) patience_counter = 0 torch.save({"epoch": epoch, "model": model.state_dict(), "best_score": best_score, "epochs_count": epochs_count, "train_losses": train_losses, "valid_losses": valid_losses}, os.path.join(args.saved_path, args.model_name + ".pth.tar")) print('**************************** TEST ****************************************') logger.info('**************************** TEST ****************************************') print("* Test for epoch {}:".format(epoch)) logger.info("* Test for epoch {}:".format(epoch))

这段代码看起来是一个训练过程中的早停（early stopping）机制，当当前的模型在验证集上的 levels_f1 分数不如之前的最佳分数（best_score）时，就将 patience_counter 增加 1，如果 patience_counter 达到了某个预设的阈值，就停止训练。如果当前的 levels_f1 分数比之前最佳分数更高，就更新最佳分数，并将 patience_counter 重置为 0。在每个 epoch 结束时，代码会输出当前模型在测试集上的表现。最后，如果当前模型的 levels_f1 分数比之前的最佳分数更高，就将当前模型保存下来。