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
Jun 12, 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
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
Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
Reinforcement learning algorithms in RLlib
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
reinforcement learning alogrithm implement with Ray
Super Mario Bros training with Ray RLlib DQN algorithm
RL training for the 6DoF manipulator
Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
Tutorial for Ray
Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
The project implements the Snake game as an OpenAI Gym environment. Deep learning is implemented using the RLlib library. A convolutional neural network is used to work with the game frames.
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."