In this paper, we explore the optimization of hyperparameters for the Soft
Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the
Tree-structured Parzen Estimator (TPE) in the context of robotic arm control
with seven Degrees of Freedom (DOF). Our results demonstrate a significant
enhancement in algorithm performance, TPE improves the success rate of SAC by
10.48 percentage points and PPO by 34.28 percentage points, where models
trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward
within 95% of the maximum reward 76% faster than without TPE, which translates
to about 40K fewer episodes of training required for optimal performance. Also,
this improvement for SAC is 80% faster than without TPE. This study underscores
the impact of advanced hyperparameter optimization on the efficiency and
success of deep reinforcement learning algorithms in complex robotic tasks.

本文探讨了使用树状 Parzen 估计器（TPE）对七自由度（DOF）机器人臂控制中的 Soft Actor-Critic（SAC）和 Proximal Policy Optimization（PPO）算法进行超参数优化的方法，在 50K 周期的训练中，实验结果显示 TPE 显著提高了算法的性能，提升了 SAC 的成功率 10.48 个百分点，提高了 PPO 的成功率 34.28 个百分点；此外，TPE 使得 PPO 在较短时间内能以最大奖励的 95% 收敛，比无 TPE 情况下快 76%，相当于节省了大约 40K 个周期的训练时间以达到最佳性能；对于 SAC 来说，优化的速度比无 TPE 的情况快 80%；本研究强调了先进的超参数优化对于复杂机器人任务中深度强化学习算法效率和成功率的影响。

优化深度强化学习用于自适应机械臂控制

Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control

Graph Neural Networks (GNNs) have shown success in various fields for
learning from graph-structured data. This paper investigates the application of
ensemble learning techniques to improve the performance and robustness of Graph
Neural Networks (GNNs). By training multiple GNN models with diverse
initializations or architectures, we create an ensemble model named ELGNN that
captures various aspects of the data and uses the Tree-Structured Parzen
Estimator algorithm to determine the ensemble weights. Combining the
predictions of these models enhances overall accuracy, reduces bias and
variance, and mitigates the impact of noisy data. Our findings demonstrate the
efficacy of ensemble learning in enhancing GNN capabilities for analyzing
complex graph-structured data. The code is public at
this https URL

本研究探讨了集成学习技术在改善图神经网络（GNNs）性能和鲁棒性方面的应用。我们通过使用多种不同的初始化或结构来训练多个 GNN 模型，创建了一个名为 ELGNN 的集成模型，该模型捕捉了数据的各个方面，并使用树状结构 Parzen 估计算法确定集成权重。结合这些模型的预测结果可以提高整体准确性，减少偏差和方差，并减轻噪声数据的影响。我们的研究结果证明了集成学习在提升 GNN 对分析复杂图结构数据的能力方面的有效性。

图神经网络的集成学习

Ensemble Learning for Graph Neural Networks

Recent advances in many domains require more and more complicated experiment
design. Such complicated experiments often have many parameters, which
necessitate parameter tuning. Tree-structured Parzen estimator (TPE), a
Bayesian optimization method, is widely used in recent parameter tuning
frameworks. Despite its popularity, the roles of each control parameter and the
algorithm intuition have not been discussed so far. In this tutorial, we will
identify the roles of each control parameter and their impacts on
hyperparameter optimization using a diverse set of benchmarks. We compare our
recommended setting drawn from the ablation study with baseline methods and
demonstrate that our recommended setting improves the performance of TPE. Our
TPE implementation is available at
this https URL

该研究提出使用树形 Parzen 估计器进行贝叶斯优化的超参数优化，对各种控制参数进行评估，推荐的设置可提高优化性能，并可从所提出的教程中获得更多信息。