avalon
ml-course
avalon | ml-course | |
---|---|---|
2 | 8 | |
169 | 2,063 | |
1.2% | 2.6% | |
1.3 | 2.4 | |
about 1 year ago | 3 days ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 only | MIT License |
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avalon
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Using AI to infer depth information from images in Godot 4 .NET 6 using the MiDaS monocular depth model
Good luck with your research! If you want to see a much more comprehensive Godot project doing pretty serious AI research, check out Avalon, which is being developed to train free-acting AI agents in realtime 3D environments.
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Godot Engine Release Management: 4.0 and beyond
If you’re interested there’s a new RL benchmark that was built using Godot (disclaimer I helped make it!)
https://github.com/Avalon-Benchmark/avalon
ml-course
What are some alternatives?
godot_rl_agents - An Open Source package that allows video game creators, AI researchers and hobbyists the opportunity to learn complex behaviors for their Non Player Characters or agents
pytorch-implementations - A collection of paper implementations using the PyTorch framework
gdrl - Grokking Deep Reinforcement Learning
IJCAI2023-CoNR - IJCAI2023 - Collaborative Neural Rendering using Anime Character Sheets
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Subway-Station-Hazard-Detection - This project is part of the CS course 'Systems Engineering Meets Life Sciences II' at Goethe University Frankfurt. In this Computer Vision project, we developed a first prototype of a security system which uses the surveillance cameras at subway stations to recognize dangerous situations. The training data was artificially generated by a Unity-based simulation.
TabularSemanticParsing - Translating natural language questions to a structured query language
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
embedml - pytorch like machine learning framework from scratch