[D] Choosing best parameters from an optimization

• GPflowOpt

  1 263 1.8 Python

  Bayesian Optimization using GPflow

  

1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.

• agents

  11 2,731 8.0 Python

  TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

  

2- You could go the reinforcement learning approach by controlling these parameters using an agent. This would mean that the parameters would have to change on the fly, which I am not sure if appropriate. If so, creating a gym environment is not so hard, which would then use something like tf.agents , rlax or any other rl framework of your liking.

• InfluxDB

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• gym

  96 33,873 0.0 Python

  A toolkit for developing and comparing reinforcement learning algorithms.

  

2- You could go the reinforcement learning approach by controlling these parameters using an agent. This would mean that the parameters would have to change on the fly, which I am not sure if appropriate. If so, creating a gym environment is not so hard, which would then use something like tf.agents , rlax or any other rl framework of your liking.

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