rl-baselines3-zoo
stable-baselines3-contrib
Our great sponsors
rl-baselines3-zoo | stable-baselines3-contrib | |
---|---|---|
11 | 6 | |
1,764 | 422 | |
4.3% | 6.7% | |
6.3 | 6.6 | |
19 days ago | 18 days ago | |
Python | 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.
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
stable-baselines3-contrib
-
Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
# https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/blob/master/sb3_contrib/tqc/tqc.py :
-
Understanding Action Masking in RLlib
Here's a theoretical overview and an implementation of action masking for PPO.
-
PPO rollout buffer for turn-based two-player game with varying turn lengths
Simplified version of rollout collection (adapted from ppo_mask.py line 282):
-
GitHub Copilot: your AI pair programmer
Transformers (GPT-3) aren't quite _supervised_, but it does require valid samples.
Agree 100% with RL being the path forward. You probably have already seen ( https://venturebeat.com/2021/06/09/deepmind-says-reinforceme... ). Personally I'm really stoked for this https://github.com/Stable-Baselines-Team/stable-baselines3-c... , which will make it a lot easier for rubes like me to use RL.
-
[P] Stable-Baselines3 v1.0 - Reliable implementations of RL algorithms
But as we already have vanilla DQN and QR-DQN (in our contrib repo: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ) I think it is already a good start for off-policy discrete action algorithms. (QR-DQN is usually competitive vs DQN+extensions)
What are some alternatives?
optuna - A hyperparameter optimization framework
muzero-general - MuZero
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
TabNine - AI Code Completions
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
stable-baselines3-c
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
copilot-cli - The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on AWS App Runner or Amazon ECS on AWS Fargate.
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
dreamerv2 - Mastering Atari with Discrete World Models
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
robot-gym - RL applied to robotics.