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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.

Marllib ⭐ 611

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

Vectorizedmultiagentsimulator ⭐ 210

VMAS is a vectorized framework 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.

⚡ ⚡ 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐

Deep Reinforcement Learning For Trading

Ray_tutorial ⭐ 55

An introductory tutorial about leveraging Ray core features for distributed patterns.

Reinforcement learning algorithms in RLlib

Mobile Env ⭐ 42

An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.

Deepcomp ⭐ 36

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

Alpyperl ⭐ 20

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

Gym_example ⭐ 15

An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial

Rl_werewolf ⭐ 12

RL environment replicating the werewolf game to study emergent communication

Adaptive Tls ⭐ 12

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

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