ml-agents
DI-engine
| ml-agents | DI-engine | |
|---|---|---|
| 60 | 3 | |
| 16,394 | 2,581 | |
| 1.0% | 6.7% | |
| 8.0 | 8.7 | |
| 4 days ago | 13 days ago | |
| C# | Python | |
| GNU General Public License v3.0 or later | 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.
ml-agents
- How do I change the maximum number of steps for training
- are the install steps update to date?
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Help with regenerating new worker id
I am a beginner to using ML Agents to simulate an environment for DL i am trying to trial runs by tinkering through different values between the action space and keep encountering this issue when attempting to run a new trial. I've tried mlagents-learn --force and mlagents-learn --run-id=newtest but both prompt the same error message. Using linux, I am aware of a similar bug occuring in older versions (https://github.com/Unity-Technologies/ml-agents/issues/1505) but solutions didn't fix it.
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Trying to get into AI
The Github page for ML-Agents has a fairly straight forward example.
- Implement API to allow AI/ML to play your game, or is it not needed?
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Is there a good alternative to Unity ML Agents?
Very few commits in the last year and not many new features (https://github.com/Unity-Technologies/ml-agents/commits/develop)
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At least I put effort into the AI prompt to generate some code that people can refer to, whereas you do absolutely nothing to contribute to the community.
and PR content: https://github.com/Unity-Technologies/ml-agents/commit/ed212103e451449bf84711a4a8f7bf11dfb1211a
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I have some questions as an absolute beginner.
Unity can build a stand-alone application or be used as a library. Javascript is deprecated, and Boo along with it although it was never really supported to begin with. Various types of machine learning are supported through the ML-Agent Toolkit and pretty well documented. The toolkit has a Python API but you should be careful about doing anything too unusual in Unity because the documentation tends to have a lot of dead-ends.
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Could Somebody please help me figure this out ? been struggling with it for a week now
Op, I'd just pull the repo again to a new folder from https://github.com/Unity-Technologies/ml-agents (use SourceTree for simplicity if you don't know git).
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Unity ML-Agents documentation is wrong, I can't build an executable and run training as the docs state
My github issue on their documentation: https://github.com/Unity-Technologies/ml-agents/issues/5899
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?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
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).
carla - Open-source simulator for autonomous driving research.
tianshou - An elegant PyTorch deep reinforcement learning library.
AssetStudio - AssetStudio is a tool for exploring, extracting and exporting assets and assetbundles.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
unity-avatar-generation - A minimal example of how to use Unity's AvatarBuilder.BuildHumanAvatar API.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents
on-policy - This is the official implementation of Multi-Agent PPO (MAPPO).