ultimate-volleyball
solve_the_spire
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ultimate-volleyball | solve_the_spire | |
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13 | 1 | |
84 | 7 | |
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0.0 | 0.0 | |
about 2 years ago | about 1 year ago | |
C# | Julia | |
Apache License 2.0 | - |
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ultimate-volleyball
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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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🏐 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.
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🏐 Training RL agents to play 3D Volleyball with Self-Play + PPO + Unity ML-Agents (learnings & link in comments)
I have lots of ideas for the project going forward (e.g. adding co-op teams, sets, fun mechanics) and am keen to run an open competition for others interested in experimenting with Unity ML-Agents together (I added a link to signup in the repo).
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.
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
TotalWarSimulator - Total War Battle simulator for AI research
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
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.
bomberman - A bomberman game!
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.