Fast and differentiable particle accelerator optics simulation for reinforcement learning and optimisation applications.

Lua-Based Machine, Deep And Reinforcement Learning Library (For Roblox And Pure Lua). Contains 34 Models!

Portfolio Optimization Library Built on top of Keras and TensorFlow Deep Learning

All the Roblox codes that were shown in AqwamCreates' YouTube channel.

This repository is used to collect papers and code in the field of AI.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

⛰ Reinforcement learning model trying to make car reach to top of mountain

Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

A collection of MARL benchmarks based on TorchRL

A space-themed OpenAI Gym environment designed for reinforcement learning. Users pilot a spaceship through an asteroid field while collecting coins. The environment increases in difficulty as the speed escalates, with asteroids and coins spawning randomly, providing dynamic challenges that are ideal for developing and refining Agents

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Repositorio donde exploro distintos algoritmos esenciales de machine learning en Python y R

Physical Symbolic Optimization

A curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project)

Temario sobre aprendizaje por refuerzo en español / Syllabus on reinforcement learning in Spanish.

Deep Reinforcement Learning in C#

Python library for solving reinforcement learning (RL) problems using generative models.

Python library for solving reinforcement learning (RL) problems using generative models (e.g. Diffusion Models).

Machine learning library, Distributed training, Deep learning, Models