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.
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Updated
Jun 11, 2024 - Python
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.
An implementation of the "gridroboman" environment from the paper "Retrieval Augmented Reinforcement Learning".
A lightweight library for PyTorch training tools and utilities
⛰ Reinforcement learning model trying to make car reach to top of mountain
World Model based Autonomous Driving Platform in CARLA 🚗
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Google Research
The Ultimate Computer Science Notes for Software Engineers and for Inquisitive Minds. 🚀
A PyTorch library for deep reinforcement learning
A collection of MARL benchmarks based on TorchRL
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
A curated list of awesome exploration RL resources (continually updated)
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Python library for solving reinforcement learning (RL) problems using generative models.
This repository is used to collect papers and code in the field of AI.
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
Hearthstone simulator using C++ with some reinforcement learning
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