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.
-
Updated
May 20, 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.
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
⚡ ⚡ 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
An introductory tutorial about leveraging Ray core features for distributed patterns.
Deep Reinforcement Learning For Trading
An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
Walkthroughs for DSL, AirSim, the Vector Institute, and more
Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
Reinforcement learning algorithms in RLlib
RLlib tutorials
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
Notebooks and exercises for the Fast Deep Reinforcement Learning Course https://courses.dibya.online/p/fastdeeprl
Add a description, image, and links to the rllib topic page so that developers can more easily learn about it.
To associate your repository with the rllib topic, visit your repo's landing page and select "manage topics."