rl-baselines-zoo
sigopt-server
| rl-baselines-zoo | sigopt-server | |
|---|---|---|
| 2 | 1 | |
| 1,106 | 33 | |
| - | - | |
| 0.0 | 9.6 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
rl-baselines-zoo
-
Agent trains great with PPO but terrible with SAC --> Advice for Hyperparameters
Take a look at these tuned sets of hyperparameters for various problems in PPO and SAC. The batch sizes are WAY smaller regardless of the problem. Your initial learning rate may also be too high.
-
How do I convert zoo / gym trained models to TensorFlow Lite or PyTorch TorchScript?
https://github.com/araffin/rl-baselines-zoo (TensorFlow based, using https://github.com/hill-a/stable-baselines)
sigopt-server
-
SigOpt (YC W15, Optimization Platform) is now fully open source
Hi, I am one of the founders of SigOpt (acquired by Intel in 2020) and I am happy to answer any questions people may have!
You can also jump right to the code here: https://github.com/sigopt/sigopt-server
What are some alternatives?
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
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.
Minigrid - Simple and easily configurable grid world environments for reinforcement learning
SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
pytorch-blender - :sweat_drops: Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.