Gymnasium
DI-engine
| Gymnasium | DI-engine | |
|---|---|---|
| 12 | 3 | |
| 5,759 | 2,581 | |
| 5.2% | 6.7% | |
| 9.3 | 8.7 | |
| 6 days ago | 13 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.
Gymnasium
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NASA JPL Open Source Rover That Runs ROS 2
"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :
[RL, MuZero reduxxxx ]
> Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments
> Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium
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Show HN: Ghidra Plays Mario
https://github.com/Farama-Foundation/Gymnasium#environments
Farama-Foundatiom/MO-Gymnasiun:
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Are there any AI projects that plays a game for you and learns?
https://github.com/Farama-Foundation/Gymnasium - A framework Python library to build and train your own AI to play games
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Unstable SAC training of sparse-reward task
The only change in the environment from the one here is the reward function which is given its return value using the following code snippet (replacing lines 648-672 in the above url):
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Any resources on experiments simulated environments?
This may be useful: https://github.com/Farama-Foundation/Gymnasium
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What's the most challenging Gym environment?
Here are all the environments. So for example, if instead of Hopper-v2 you want the acrobat environment from classic control you can write: env = gym.make('Acrobot-v1')
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Gymnasium 0.28 is now released
This release also includes a large number of documentation updates, minor bug fixes, and other minor improvements; the full release notes are available here if you’d like to learn more: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.28.0.
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TransformerXL + PPO Baseline + MemoryGym
Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.
- Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
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[N] Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
You can read the release notes here: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.27.0. You can upgrade from 0.26 without any changes unless you're doing something very uncommon; this is how releases will generally be going forward.
DI-engine
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Anyone have experience with DI-Engine?
I posted a while back asking people what frameworks they were using for RL research. Recently i stumbled upon DI-Engine which looks promising! Actively maintained, with a diverse set of algorithms already implemented.
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TransformerXL + PPO Baseline + MemoryGym
DI Engine
- Struggling with algorithm generality? Try DI engine; here is the solution
What are some alternatives?
flake8 - The official GitHub mirror of https://gitlab.com/pycqa/flake8
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Flake8-pyproject - Flake8 plug-in loading the configuration from pyproject.toml
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
ruff - An extremely fast Python linter and code formatter, written in Rust.
tianshou - An elegant PyTorch deep reinforcement learning library.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
Visual Studio Code - Visual Studio Code
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory
on-policy - This is the official implementation of Multi-Agent PPO (MAPPO).