optuna
rl-baselines3-zoo
Our great sponsors
| optuna | rl-baselines3-zoo | |
|---|---|---|
| 34 | 11 | |
| 9,640 | 1,777 | |
| 3.4% | 5.0% | |
| 9.9 | 6.3 | |
| 1 day ago | 25 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
optuna
-
Optuna – A Hyperparameter Optimization Framework
I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.
Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...
- How to test optimal parameters
- FOSS hyperparameter optimization framework to automate hyperparameter search
-
How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
- How to tune more than 2 hyperparameters in Grid Search in Python?
-
Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
-
Best practices for training PyTorch model
Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration
-
[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
rl-baselines3-zoo
-
Can't solve MountainCar-v0 with A2C algorithm (stable-baselines3)
I'm trying to solve MountainCar-v0 enviroment from gymnasium with the A2C algorithm and the agent doesn't find a solution. I checked this so I added import stable_baselines3.common.sb2_compat.rmsprop_tf_like as RMSpropTFLike. Also checked the rl-baselines3-zoo for the hyperparameter tuning. So my code is:
-
Stable-Baselines3 v2.0: Gymnasium Support
RL Zoo3 (training framework): https://github.com/DLR-RM/rl-baselines3-zoo
-
Tips and Tricks for RL from Experimental Data using Stable Baselines3 Zoo
I'm still new to the domain but wanted to shared some experimental data I've gathered from massive amount of experimentation. I don't have a strong understanding of the theory as I'm more of a software engineer than data scientist, but perhaps this will help other implementers. These notes are based on Stable Baselines 3 and RL Baselines3 Zoo with using PPO+LSTM (should apply generally to all the algos for the most part)
-
Simple continuous environment with spaceship but yet challenging for RL algorithms (like SAC, TD3)
Try hyperparameter search. It's implemented here: https://github.com/DLR-RM/rl-baselines3-zoo for stable-baselines3. Hyperparameters make a huge difference in RL, much more than in supervised learning.
-
Easily load and upload Stable-baselines3 models from the Hugging Face Hub 🤗
Integrating RL-baselines3-zoo
-
Help comparing Double DQN against another paper's results
Hello, I've been running some tests of Double DQN with Stable Baselines 3 Zoo and to compare I'm using the graphs provided by Noisy Networks For Exploration.
-
DDPG not solving MountainCarContinuous
- you can find tuned hyperparameters for DDPG, SAC, PPO in https://github.com/DLR-RM/rl-baselines3-zoo
-
Hyperparameter tuning examples
For more complete implementation: https://github.com/DLR-RM/rl-baselines3-zoo
-
How do I convert zoo / gym trained models to TensorFlow Lite or PyTorch TorchScript?
https://github.com/DLR-RM/rl-baselines3-zoo (PyTorch based, using https://github.com/DLR-RM/stable-baselines3)
-
[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
We also release 100+ trained models in our experimental framework, the rl zoo: https://github.com/DLR-RM/rl-baselines3-zoo
What are some alternatives?
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
pg_plan_advsr - PostgreSQL extension for automated execution plan tuning
rl-trained-agents - A collection of pre-trained RL agents using Stable Baselines3