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
Aug 6, 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.
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
Reinforcement learning algorithms in RLlib
Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
Super Mario Bros training with Ray RLlib DQN algorithm
RL environment replicating the werewolf game to study emergent communication
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Tutorial for Ray
Training in bursts for defending against adversarial policies
Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
RL training for the 6DoF manipulator
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