Optuna Tuning Results PPO Reinforcement Learning Hyperparameters Performance

Systematic hyperparameter tuning using Optuna was expected to improve PPO model performance in a multi-microgrid environment. We hypothesized that optimizing hyperparameters like learning rate and network architecture would enhance model performance, reflected in increased mean reward and training stability.

Data Overview:

Dataset: Results from hyperparameter tuning of a PPO model in a multi-microgrid environment
Contents: Hyperparameter settings and performance metrics.

Data Collection Process:

Sampling: Hyperparameters sampled by Optuna and tested by training PPO for 500,000 timesteps

Use the command tensorboard --logdir=./Logs/PPO_1 to visualize the data with TensorBoard.

预期通过 Optuna 进行的系统化超参数调优将提升多微电网环境中 PPO 模型的性能。我们假设优化学习率、网络架构等超参数将增强模型性能，体现在平均奖励的增加和训练稳定性的提升。

数据概览：
数据集：多微电网环境中 PPO 模型超参数调优的结果
内容：超参数设置和性能指标。

数据收集过程：
样本：Optuna 采样的超参数，通过训练 PPO 进行 500,000 个时间步长的测试。

使用命令 tensorboard --logdir=./Logs/PPO_1 可通过 TensorBoard 可视化数据。