Obstacle Tower Environment

Reinforcement Learning Algorithms Based on PyTorch

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

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

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

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

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.

📽 Python package to live stream ML-Agents training process from Google Colab to Twitch/YouTube server.

Reinforcement Learning with Robot Arm

Gaussian process optimization using GPyOpt for Unity ML-Agents Toolkit

AINE-DRL is a deep reinforcement learning (DRL) baseline framework. AINE means "Agent IN Environment".

OpenAI Gym interface for NavOps environment.

Solve reacher (unity ml-agents) using deep deterministic policy gradients (DDPG)

Reinforcement learning algorithms implemented based on tensorflow 1.x

Continuous control for Unity ML-Agents Reacher & Crawler environment.

This project uses Deep Q Network(DQN) to train an agent to navigate a large, square world to collect yellow bananas and avoid blue bananas.

Multi-Agent reinforcement learning with Unity ML-Agents Tennis environment.

Deep Deterministic Policy Gradient

PyTorch application of reinforcement learning DDPG and PPO algorithms in Unity 3D-Ball

Proximal Policy Optimization using Pytorch and the Unity Reacher environment.