#大语言模型#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
Artificial Life and Intelligence. Researching behavior by nature and nurture simulation, deploying multi-agent reinforcement learning and evolving generational inheritance.