There are 5 repositories under rllib topic.
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.
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.
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
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
Super Mario Bros training with Ray RLlib DQN algorithm
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
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
JupyterLab Notebook for Mesosphere DC/OS
Training in bursts for defending against adversarial policies
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.
Comparison of different Deep Reinforcement Learning (DRL) Frameworks. This repository includes "tf-agents", "RLlib" and will soon support "acme" as well.
Tutorial for Ray
My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].
Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning), Mesa (Agent-based modeling) and OpenAI Gym.
Solutions to the Harvest CPR appropriation problem with policy gradient methods and social learning, for Autonomous and Adaptive Systems class at UNIBO
Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
Learning various robotic manipulations tasks of the UR3
Deep Learning and Computational Intelligence final project (5.0) - Application of reinforcement learning for optimization of a racing line of a F1 car
Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
ray project 中文文档
Multi Agent Reinforcement Learning in a Predator-Prey-Grass environment