auto-sklearn
on-policy
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| auto-sklearn | on-policy | |
|---|---|---|
| 3 | 12 | |
| 7,388 | 1,101 | |
| 0.6% | 5.8% | |
| 1.8 | 5.4 | |
| 4 months ago | 2 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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auto-sklearn
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What free AutoML library do you recommend?
If you want a more stable AutoML library, i’ll suggest auto-sklearn which optimises performance of sklearn learning algorithms.
on-policy
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"chmod" is not recognized as an internal or external command, operable program or batch file
I am trying to run this code (https://github.com/marlbenchmark/on-policy) on my Windows machine. Everything is successful until section 3, where:
If you don't want to install a Linux VM, the other option is to read the source of the train_mpe.sh script and write your own version as a Windows batch file.
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Why is this implementation of PPO using a replay buffer?
I don't see the buffer being cleared anywhere, but it looks to me like it may not need to... For example, the implementation of SeparatedReplayBuffer receives the episode_length (or "horizon" as is sometimes called) and sets the size of the buffer accordingly when its initialized. That way, the amount of samples collected before each policy/value update is constant. You just need one giant tensor block to collect all your samples, then after doing a networks update, why clear them out? Just overwrite the existing samples, since you know you'll collect exactly the same number of new samples.
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MARL top conference papers are ridiculous
https://github.com/marlbenchmark/on-policy (MAPPO-FP)
What are some alternatives?
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
tune-sklearn - A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
syne-tune - Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
DI-engine - OpenDILab Decision AI Engine
iterative-stratification - scikit-learn cross validators for iterative stratification of multilabel data
DIgging - Decision Intelligence for digging best parameters in target environment.