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I'm trying to perform an hyperparameter tuning on a SAC algorithm (Stable Baselines 3) with optuna lib.

I guess hyperparameters have different significance and typical ranges depending of the selected algorithm.

It can be found for PPO here : https://medium.com/aureliantactics/ppo-hyperparameters-and-ranges-6fc2d29bccbe

I wonder what are importance and typical ranges for learning_rate, buffer_size, batch_size, tau, gamma, train_freq, gradient_steps, action_noise, ent_coef with SAC, DDPG and TD3.

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