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hyperparameter-optimization

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ray-project / ray

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[RLlib] Policy weights overwritten in self-play for local_mode

george-skal
george-skal commented Jun 28, 2021

Hi all!
I am trying a self-play based scheme, where I want to have two agents in waterworld environment have a policy that is being trained (“shared_policy_1”) and other 3 agents that sample a policy from a menagerie (set) of the previous policies of the first two agents ( “shared_policy_2”).
My problem is that I see that the weights in the menagerie are overwritten in every iteration by the cur

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P2 bug good first issue rllib
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nni

microsoft / nni

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An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

python data-science machine-learning deep-learning neural-network tensorflow machine-learning-algorithms pytorch distributed feature-extraction hyperparameter-optimization feature-engineering nas bayesian-optimization automl automated-machine-learning model-compression neural-architecture-search deep-neural-network automated-feature-engineering
  • Updated Jul 2, 2021
  • Python

optuna / optuna

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awslabs / autogluon

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Gradient-Free-Optimizers

SimonBlanke / Gradient-Free-Optimizers

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Grid search variant

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SimonBlanke
SimonBlanke commented Mar 9, 2021

I think it would be useful to have a grid search optimizer in this package. But its implementation would probably be quite different from other ones (sklearn, ...).

The requirements are:

  • The grid search has to stop after n_iter instead of searching the entire search space
  • The positions should not be precalculated at the beginning of the optimization (i have concerns about memory load).
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enhancement good first issue help wanted

guan-yuan / awesome-AutoML-and-Lightweight-Models

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A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

tensorflow pytorch hyperparameter-optimization awesome-list quantization nas automl model-compression neural-architecture-search meta-learning architecture-search quantized-training model-acceleration automated-feature-engineering quantized-neural-network
  • Updated Jun 19, 2021

LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

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Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband
  • Updated Jun 21, 2021
  • Jupyter Notebook

HDI-Project / ATM

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Avoid creating redundant datasets

bcyphers
bcyphers commented Jan 31, 2018

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing

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enhancement good first issue help wanted low priority
Neuraxle

Neuraxio / Neuraxle

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Code Style: Improve Code Style

guillaume-chevalier
guillaume-chevalier commented Jun 28, 2021

Describe the bug
Code could be more conform to pep8 and so forth.

To Reproduce
https://app.codacy.com/gh/Neuraxio/Neuraxle/issues?&filters=W3siaWQiOiJMYW5ndWFnZSIsInZhbHVlcyI6W119LHsiaWQiOiJDYXRlZ29yeSIsInZhbHVlcyI6WyJDb2RlU3R5bGUiXX0seyJpZCI6IkxldmVsIiwidmFsdWVzIjpbXX0seyJpZCI6IlBhdHRlcm4iLCJ2YWx1ZXMiOltdfSx7ImlkIjoiQXV0aG9yIiwidmFsdWVzIjpbXX1d

Expected behavior
Less code st

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documentation good first issue invalid
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amanchadha / coursera-deep-learning-specialization

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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

deep-learning neural-network coursera recurrent-neural-networks neural-networks hyperparameter-optimization regularization convolutional-neural-networks neural-machine-translation coursera-machine-learning convolutional-neural-network hyperparameter-tuning andrew-ng coursera-assignment cnns rnns andrew-ng-course recurrent-neural-network neural-style-transfer coursera-specialization
  • Updated Jul 3, 2021
  • Jupyter Notebook

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