ultimate-volleyball
slimevolleygym
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| ultimate-volleyball | slimevolleygym | |
|---|---|---|
| 13 | 3 | |
| 84 | 698 | |
| - | - | |
| 0.0 | 3.2 | |
| about 2 years ago | 5 months ago | |
| C# | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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
slimevolleygym
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How to train multi agents with PPO/DQN for playing Atari Game
This is a good example for self-play training Slime Volleyball
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RL framework for 2v2 kart soccer
Hi great that you are interested in the area, but as a beginner project is quite complex, having a team is a multi-agent task so not a small feat and i guess you want the same policy to play against itself? what is know as selfplay. which is not so hard to understand but a little bit in the tech part. Look a this 1v1 environment has a tutorial where they show selfplay and other single agent approaches using a well known RL Pytorch implementations. and for the policy optimization algorithm as the tutorial before you should go with PPO (which is a on-policy method like reinforce). there is something called HER for sparse reward but it works with off-policy methods like ddpg or sac. read a little bit more about this and then you will get the idea. My suggestion if you dont have extend experience try a supervise learning approach, where you have a dataset where the action is the label to be predicted and the observation is the input, MSE for the loss. like predicting the stering wheel angle from the image of the road kind of setup.
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🏐 Ultimate Volleyball: A 3D Volleyball environment built using Unity ML-Agents
Inspired by Slime Volleyball Gym, I built a 3D Volleyball environment using Unity's ML-Agents toolkit. The full project is open-source and available at: 🏐 Ultimate Volleyball.
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.
bomberland - Bomberland: a multi-agent AI competition based on Bomberman. This repository contains both starter / hello world kits + the engine source code
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
TotalWarSimulator - Total War Battle simulator for AI research
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
RoboLeague - A car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting.
SimpleGOAP - SimpleGOAP is a lightweight C# implementation of goal oriented action planning.
solve_the_spire - An AI to play the video game Slay the Spire
bomberman - A bomberman game!
unity-ml-agents-turret-defense - A reinforcement learning agent playing as the turret, where its goal is to allow ten friendly units to enter the base, and loses if an enemy unit has entered the base or if two friendly units were shot.