aquarium
rl-baselines3-zoo
| aquarium | rl-baselines3-zoo | |
|---|---|---|
| 2 | 11 | |
| 57 | 1,796 | |
| - | 3.6% | |
| 4.7 | 6.2 | |
| about 1 month ago | 14 days ago | |
| Ruby | Python | |
| MIT License | 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.
aquarium
-
What are your challenges with the existing electronic lab notebooks?
For what it’s worth, I was playing around with this a little bit: https://www.aquarium.bio many of the authors are currently in the synthetic biology software space.
-
New Study Explains How to Engineer the Coronavirus + All Other Synthetic Biology Research This Week
From the methods section of the Aquarium paper: Aquarium is distributed under the open-source MIT license. Aquarium, documentation, and installation instructions are freely available (https://www.aquarium.bio/) along with links to Dockerized versions of the software. Code is maintained on Github (https://github.com/aquariumbio/aquarium). Aquarium’s Python API (Trident) is also under the open-source MIT license and is hosted on the open-source python repository at PyPI (https://pypi.org/project/pydent/) and its documentation and installation instructions are also freely available (https://aquariumbio.github.io/trident/).
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?
optuna - A hyperparameter optimization framework
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
rl-trained-agents - A collection of pre-trained RL agents using Stable Baselines3
gym - A toolkit for developing and comparing reinforcement learning algorithms.
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
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.
softlearning - Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
space-gym - Challenging reinforcement learning environments with locomotion tasks in space