solve_the_spire
ultimate-volleyball
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solve_the_spire | ultimate-volleyball | |
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1 | 13 | |
7 | 84 | |
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0.0 | 0.0 | |
about 1 year ago | about 2 years ago | |
Julia | C# | |
- | Apache License 2.0 |
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solve_the_spire
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Show HN: Bomberland – An AI competition to build the best Bomberman bot
https://github.com/DevJac/solve_the_spire
I stretched the truth a bit, I'm actually doing something like "hierarchical model-free reinforcement learning", even so, figuring out how to break the game down to create a hierarchy of agents is a lot of work. Basically, the AI is composed of about 8 different traditional RL agents (neural networks), each deciding a different thing. One chooses which cards to draft, one chooses which actions to take in combat, one chooses which path to take on the map, etc.
It shows definite signs of improvement, but has only reached a point where it can beat the act 1 boss about 50% of the time. I think that is its limit right now. I'm doing policy gradient which is very sample inefficient. I'm going to implement soft-actor-critic and see if it can do better with better sample efficiency.
And to reiterate my original point, I think each developer only has one or two reverse engineering attempts in them. I might otherwise be interested in this AI competition, but reverse engineering the environment to create my own model is just too daunting, I'm so burned out on it already.
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
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
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.
bomberman - A bomberman game!
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.
slimevolleygym - A simple OpenAI Gym environment for single and multi-agent reinforcement learning
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.