#计算机科学#Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
翻译 - 一个快速简单的框架，用于构建和运行分布式应用程序。 Ray与RLlib（可扩展的强化学习库）和Tune（可扩展的超参数调整库）打包在一起。
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 ...
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 open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
#计算机科学#Deep Reinforcement Learning For Trading
An introductory tutorial about leveraging Ray core features for distributed patterns.
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
#计算机科学#Reinforcement learning algorithms in RLlib
#计算机科学#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
Used Flow, Ray/RLlib and OpenAI Gym to simulate and train autonomous vehicles/human drivers in SUMO (Simulation of Urban Mobility)
RL environment replicating the werewolf game to study emergent communication
A Predator-Prey-Grass multi-agent gridworld environment implemented with Farama's Gymnasium and PettingZoo. Featuring dynamic spawning and deletion and partial observability of agents.