reinforcement-learning-discord-wiki VS cleanrl

There are a few good subreddits that I could recommend. If you want to go in the direction of RL then /r/reinforcementlearning is the one and I would highly suggest that you join the Discord server as well. People are really friendly there and provide a lot of help if you have questions.

This discord is pretty good https://github.com/andyljones/reinforcement-learning-discord-wiki/wiki

This is also a good resource if you are facing other problems.

We've got an active Discord here. Isn't explicitly a study group, but has plenty of other newbies asking questions.

Thank you for the kind words! I am also quite excited about the new points in game design space that RL will unlock and am planning write another blogpost on that topic.

I quite like https://karpathy.github.io/2016/05/31/rl/ as an introduction to some of the ideas behind modern RL. Beyond that, I just recently found out about https://github.com/andyljones/reinforcement-learning-discord... which lists a lot of other high-quality resources.

CodeCraft is a programming which you can "play" by writing a Scala/Java program that controls the game units. It's not actively developed anymore but still functional: http://codecraftgame.org/

This article is a collection of debugging advice that has served me well over the past few years. It's collated both from my personal experiences, and from several months of discussion in the RL Discord. It is intended as compliment to the other excellent advice that can be found elsewhere.

Every Saturday on the RL Discord we host a talk in the voice channel by a server regular. The talk is typically 10-30 mins long with questions and open discussion afterwards.

We've collated some recommendations on the RL Discord wiki.

There's a selection of popular resources on the RL Discord wiki.

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)