ultimate-volleyball
ml-agents
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| ultimate-volleyball | ml-agents | |
|---|---|---|
| 13 | 60 | |
| 84 | 16,324 | |
| - | 1.7% | |
| 0.0 | 8.1 | |
| about 2 years ago | 5 days ago | |
| C# | C# | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
ultimate-volleyball
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Volleyball agents trained using competitive self-play [tutorial + project link]
As linked in the tutorial, a Unity ML environment: https://github.com/CoderOneHQ/ultimate-volleyball
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Competitive self-play with Unity ML-Agents
The latest version of the Ultimate Volleyball repo (or, you can use your own volleyball environment if you've been following the tutorial series)
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Show HN: Bomberland – An AI competition to build the best Bomberman bot
No others (at the moment). We're a small team so Bomberland is our current focus - we want to improve the tooling first so that it's easy for people to dive into ML before we introduce other environments.
We do have a mini-project called Ultimate Volleyball (https://github.com/CoderOneHQ/ultimate-volleyball) built on Unity ML-Agents. It's intended more as an introduction to deep reinforcement learning, and we wrote some tutorials for it here if anyone's interested: https://www.gocoder.one/blog/hands-on-introduction-to-deep-r...
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How to train agents to play volleyball using deep reinforcement learning
Ultimate Volleyball Repo
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Design reinforcement learning agents using Unity ML-Agents
If you get stuck, check out the pre-configured BlueAgent , or see the full source code in the Ultimate Volleyball project repo.
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Bomberland: a 2D multi-agent environment for AI agents based on Bomberman
We're launching an upcoming project called Coder One where you can build agents to compete in a 2D game based on Bomberman.
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A multi-agent artificial intelligence playground [Looking for feedback]
If you're interested in checking it out, this is the website: https://www.gocoder.one
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A hands-on introduction to deep reinforcement learning using Unity ML-Agents
In this series, I'll walk you through how to use Unity ML-Agents to build a volleyball environment and train agents to play in it using deep RL. For a bit of fun and extra incentive, you'll be able to submit your trained agent to the Ultimate Volleyball leaderboard and have it compete against other agents.
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Last week fluff-free AI, ML, and data-related original articles summary
- Elon Musk unveils Tesla Bot, a humanoid robot that uses vehicle AI read - Multi-agent reinforcement learning environment built on Unity ML-Agents link read - Up to 40% of GitHub Copilot's generated code can be vulnerable read
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[P] A 3D Volleyball reinforcement learning environment built with Unity ML-Agents
Project: Link
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
What are some alternatives?
bomberland - Bomberland: a multi-agent AI competition based on Bomberman. This repository contains both starter / hello world kits + the engine source code
gym - A toolkit for developing and comparing reinforcement learning algorithms.
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
TotalWarSimulator - Total War Battle simulator for AI research
carla - Open-source simulator for autonomous driving research.
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
AssetStudio - AssetStudio is a tool for exploring, extracting and exporting assets and assetbundles.
RoboLeague - A car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting.
unity-avatar-generation - A minimal example of how to use Unity's AvatarBuilder.BuildHumanAvatar API.
SimpleGOAP - SimpleGOAP is a lightweight C# implementation of goal oriented action planning.
tensortrade - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.