rllib

#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.

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 ...

⚡ ⚡ 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 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.

Walkthroughs for DSL, AirSim, the Vector Institute, and more

Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning

RLlib tutorials

Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning

#计算机科学#Reinforcement learning algorithms in RLlib

An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium

#计算机科学#An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial

Tutorial for Ray

Super Mario Bros training with Ray RLlib DQN algorithm

Intelligent Artificial Life. Researching behavior by nature and nurture simulation, deploying multi-agent reinforcement learning and evolving generational inheritance.

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