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.