reinforcement-learning

ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning

Deep Reinforcement Learning: Zero to Hero!

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model. NeurIPS 2024 Spotlight.

An End-To-End, Lightweight and Flexible Platform for Game Research

Reinforcement Learning environments based on the 1993 game Doom :godmode:

Advanced Deep Learning with Keras, published by Packt

Physical Symbolic Optimization

🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!

Developer Asset Hub for NVIDIA Nemotron — A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples to build with Nemotron models

Implementation of all RL algorithms in a simpler way

Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning

A Platform for Many-Agent Reinforcement Learning

A lightweight library for PyTorch training tools and utilities

🙄 Difficult algorithm, Simple code.

Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback

AgentEvolver: Towards Efficient Self-Evolving Agent System

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

Proximal Policy Optimization (PPO) algorithm for Super Mario Bros