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
May 20, 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.
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.
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
Wrappers for reinforcement learning algorithms (i.e. stable baselines 3, RLlib) to work with pyRDDLGym.
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Reinforcement learning algorithm that blends the N-th order Markov property with abstract MDPs, PPO, and a hybrid model-free/model-based approach.
Reinforcement learning algorithms in RLlib
Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
Tutorial for Ray
Reinforcement Learning for Unmanned Airial Vehicles
Utiliza algoritmos de RAY[rllib] no ambiente KAS (PettingZoo) com observação em imagem para treinar um agente inteligente. Utiliza CNN (Pytorch) e wrappers da SuperSuit para processar a observação em imagem.
A template for deploying DreamerV3 with Ray RLlib, compatible with Gym and custom environments.
Deep Reinforcement Learning For Trading
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
Balloon Flight Custom Ray environment
Reinforcement learning for Warlock Brawl, and an ECS implementation of Warlock in TypeScript
Emergent Communication in RLlib
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
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