ultimate-volleyball
SimpleGOAP
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| ultimate-volleyball | SimpleGOAP | |
|---|---|---|
| 13 | 1 | |
| 84 | 16 | |
| - | - | |
| 0.0 | 10.0 | |
| about 2 years ago | about 2 years ago | |
| C# | C# | |
| 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
SimpleGOAP
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GOAP (Goal-Oriented Action Planning) is absolutely terrific.
If anyone is looking for a simple C# GOAP library, I wrote one earlier this year for fun. I haven’t used it myself for gamedev yet, but it should be quite easy to integrate into Godot Mono: https://github.com/tckerr/SimpleGOAP
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.
ReGoap - Generic C# GOAP (Goal Oriented Action Planning) library with Unity3d examples
bomberland - Bomberland: a multi-agent AI competition based on Bomberman. This repository contains both starter / hello world kits + the engine source code
ai-series-part-14.5 - Project used in the AI Series Part 14.5 Unity Tutorial where I show how to approach optimizing code, using our runtime navmesh generation as the optimization target
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
ai-series-part-24 - Tutorial repository for AI Series Part 24, which is the 4th and final part of a sub-series to implement enemy skills and abilities. In this repository we have extended the foundation from part 21, 22, and 23 to implement a new instant-cast ability - poison gas
TotalWarSimulator - Total War Battle simulator for AI research
ai-series-part-20 - This tutorial repository is the end state of the AI Series Part 20 - Weighted Random Spawning video where we implement a new spawn method and define these spawn configurations in ScriptableObjects.
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
ai-series-part-14 - Project corresponding to AI Series Part 14 where we implement Baking NavMesh at Runtime around the Player, instead of baking on the entire scene
RoboLeague - A car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting.
ai-series-part-31 - learn how to show a path to a specific target without using a NavMeshAgent. We'll calculate the path on a NavMesh from the player's current location (controlled via a Third Person Controller) to the target location, and show that with a LineRenderer. The path calculation has a few knobs to turn, how high above the NavMesh to draw the line and how frequently to recalculate the path.