wandb VS cleanrl

Weights & Biases — The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management. Free tier for personal projects only, with 100 GB of storage included.

HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"

Wandb is the best! https://wandb.ai/

To track stuff I've used wandb.ai in a company in the past, as someone else pointed out. Regarding metrics... This is really specific to your domain, and it is such a broad question. You could count color pixels, the distribution of intensity histograms, etc etc.

Step 1: The colab notebook uses wandb.ai, so you need to register for a wandb.ai account beforehand if you want to use the colab notebook. After registering you need to go to your homepage and copy the API key and paste/keep it somewhere.

I want to take a look at tools like https://wandb.ai/ and they would integrate into some of the pipelines I'm playing with.

does current cgi and ai tech can bring back leslie nielsen? might use unreal engine and https://www.resemble.ai/ or https://wandb.ai/?

I'm currently working for a MLOps company so I'm heavily using their tools (Weights & Biases) but I've used custom C++ for deployment, Pytorch + fastai for quick experimentation, Weights & Biases for experiment tracking, hyper-parameter tuning + model versioning (hence why I went to work for them), custom database + data pipeline, HoloViz for data visualisation (really nice dashboarding tool), Jenkins for CI/CD, I also love Github Actions.

PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.

Here's where the problem starts: after implementing a custom environment that follows the typical gymnasium interface, and use a slightly adjusted PPO implementation from CleanRL, I cannot get the agent to learn anything at all, even though this specific implementation seems to work just fine on basic gymnasium examples. I am hoping the RL community here can help me with some useful pointers.

Try out a standard implementation with some standard parameters from here: https://github.com/vwxyzjn/cleanrl/tree/master/cleanrl

I am trying to run cleanrl on the `Pendulum-v1` environment. I did that by going here and changing the default `env-id` to ` parser.add_argument("--env-id", type=str, default="Pendulum-v1",

git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl poetry install

Can someone please help me debug my implementation of SAC. Please let me know if you have any questions. I tried comparing my work with CleanRL and caught a couple of errors. However, my implementation does diverge a lot from theirs as I wanted to test my understanding.

Shameless self-plug: as far as implementation is concerned, I am working on a (hopefully) easier to understand Dreamer architecture under the CleanRL library, toward also re-implementing Director, Dreamer-v3, and and JAX variant for faster training.

Happy to share that CleanRL now has a new algorithm called Robust Policy Optimization — 5 lines of code change to PPO to get better performance in 57 out of 61 continuous action envs 🚀 (e.g., dm_control)