skrl VS cleanrl

skrl will allow you to easily configure and use off-policy algorithms such as DDPG, TD3 and SAC in Isaac Gym, Omniverse Isaac Gym and Isaac Orbit, but I think there will not be significant gains compared to on-policy algorithms.

Check its comprehensive documentation at https://skrl.readthedocs.io

Also, skrl. It supports RNN, LSTM, GRU, and other variants for A2C, DDPG, PPO, SAC, TD3, and TRPO agents. See the models basic usage and examples

In this case, I encourage you to try the skrl RL library that fully supports all of them, among others.

I encourage you to try skrl (https://skrl.readthedocs.io).

I encourage you to take a look at the skrl library...

This is the list of examples (to be included in the documentation) that includes RNN: (ddpg_gym_pendulumnovel_gru.py, ddpg_gym_pendulumnovel_lstm.py, ddpg_gym_pendulumnovel_rnn.py, etc.)... and here are some RNN benchmarking results (to be updated for the release)

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)