ml-agents

Repo for the Deep Reinforcement Learning Nanodegree program

Obstacle Tower Environment

Reinforcement Learning Algorithms Based on PyTorch

An simple, reliable, and minimal implementation of the AI CoScientist Paper from Google "Towards an AI co-scientist" with Swarms Framework

Various examples of ml-agents while teaching concepts about it in YouTube

UAV Logistics Environment for Multi-Agent Reinforcement Learning / Unity ML-Agents / Unity 3D

Animal-AI supports interdisciplinary research to help better understand human, animal, and artificial cognition.

A simple example of how to implement vector based DQN using PyTorch and a ML-Agents environment

Showcase environment for ML-Agents

A Complete Open Source Agent AI Framework For Unity

Train reinforcement learning agent using ML-Agents with Google Colab.

A Self Play reinforcement learning Agent learns to play TicTacToe using the ML-Agents Framework in Unity.

Repository for slides & codes of RL Korea Bootcamp

Research into Assault Course for training Active Ragdolls (using MujocoUnity+ml_agents)

Motorcycle Racing made with Unity Machine Learning Agents

Official implementation of paper "CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning" by M. Chancán (ICRA 2020) https://doi.org/10.1109/ICRA40945.2020.9197336

A simple example of how to implement vector based DDPG for MARL tasks using PyTorch and a ML-Agents environment.

The Mayan Adventure is an open-source reinforcement learning environment for Unity ML-Agents. In this environment, you train your agent (Indie) to find the golden statue in this dangerous environment full of traps.

This repository documents my journey to become a Machine Learning Engineer. It contains the list of projects undertaken, books read, courses pursued and almost everything I did in the process. So follow along or diverge to you own path, each way, I believe this will add value to any data professional / enthusiast.

Unofficial, Google‑free reimplementation of MLE‑STAR: a lightweight, local‑friendly multi‑agent ML engineering pipeline that uses OpenAI‑compatible LLMs (OpenRouter/Ollama) and DuckDuckGo search to generate, debug, refine, ensemble, and submit Kaggle‑ready solutions—AutoML for tabular tasks with code generation and iterative refinement.