The tuning of hyperparameters in reinforcement learning (RL) is critical, as these parameters significantly impact an agent's performance and learning efficiency. Dynamic adjustment of hyperparameters during the training process can significantly enhance both the performance and stability of learning. Population-based training (PBT) provides a method to achieve this by continuously tuning hyperparameters throughout the training. This ongoing adjustment enables models to adapt to different learning stages, resulting in faster convergence and overall improved performance. In this paper, we propose an enhancement to PBT by simultaneously utilizing both first- and second-order optimizers within a single population. We conducted a series of experiments using the TD3 algorithm across various MuJoCo environments. Our results, for the first time, empirically demonstrate the potential of incorporating second-order optimizers within PBT-based RL. Specifically, the combination of the K-FAC optimizer with Adam led to up to a 10% improvement in overall performance compared to PBT using only Adam. Additionally, in environments where Adam occasionally fails, such as the Swimmer environment, the mixed population with K-FAC exhibited more reliable learning outcomes, offering a significant advantage in training stability without a substantial increase in computational time.

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