ultimate-volleyball
bomberland
Our great sponsors
| ultimate-volleyball | bomberland | |
|---|---|---|
| 13 | 2 | |
| 84 | 100 | |
| - | 2.0% | |
| 0.0 | 3.1 | |
| about 2 years ago | 3 months ago | |
| C# | C++ | |
| Apache License 2.0 | 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.
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
bomberland
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Show HN: Bomberland – An AI competition to build the best Bomberman bot
Thanks for the feedback! We're working on improving the onboarding flow. Sorry about the Docker link issue - it should link you to a copy of the environment binary so that you can play around without Docker (no docs for this workflow just yet unfortunately).
It's essentially a Bomberman-inspired game, where you program the agents to play in it and can play against other users' agents. You can try it out without an account by cloning one of the starter kits here: https://github.com/CoderOneHQ/bomberland and following the usage instructions (but you'll need to create an account to use the visualizer and to submit agents).
We recommend the Docker flow, but if you get stuck feel free to reach out to me (Joy) or @thegalah (Matt) on our Discord: https://discord.gg/NkfgvRN
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Bomberland: a new artificial intelligence competition
We have starter kits in Python and TypeScript to help you get started (and encourage any community contributions to the starter kit repo).
What are some alternatives?
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
q-learning-algorithms - This repository will aim to provide implementations of q-learning algorithms (DQN, Double-DQN, ...) using Pytorch.
TotalWarSimulator - Total War Battle simulator for AI research
control-flag - A system to flag anomalous source code expressions by learning typical expressions from training data
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
solve_the_spire - An AI to play the video game Slay the Spire
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
iNeural - A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms.
RoboLeague - A car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting.
loneliless - A Deep-Q Network playing a single player Pong game. Network done in Python (Tensorflow-gpu) with the single player Pong game implemented in C++ (Openframeworks) and both binded with Pybind11.
SimpleGOAP - SimpleGOAP is a lightweight C# implementation of goal oriented action planning.
PythonMonkey - A Mozilla SpiderMonkey JavaScript engine embedded into the Python VM, using the Python engine to provide the JS host environment.