Awesome GitHub RepositoriesDistributed RL Scaling

TensorTrade is a reinforcement learning trading framework designed for training and deploying autonomous agents that optimize financial market strategies. It provides an algorithmic trading simulation environment where agents can be tested against market data using simulated broker environments. The framework features a distributed training system using RLlib to optimize decision policies across large datasets. It includes a walk-forward validation tool that evaluates trading strategies through windowed performance analysis to prevent overfitting and measure real-world viability. The project

SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The fra

Acme ist ein Framework für Reinforcement Learning und eine Ausführungsumgebung, die für die Entwicklung und das Benchmarking von Lernalgorithmen konzipiert wurde. Es bietet eine Bibliothek modularer Komponenten und Referenzimplementierungen, mit denen Agenten erstellt und Performance-Baselines etabliert werden können. Das System ermöglicht die Skalierung von Agenten-Architekturen von der Single-Stream-Ausführung bis hin zu großen verteilten Umgebungen. Dies erlaubt den Übergang vom ersten Prototyping zur verteilten Ausführung für Training und Evaluierung. Das Framework deckt die Entwicklung von Reinforcement Learning und das Prototyping von Agenten-Architekturen ab und liefert die notwendigen Bausteine, um neue Modelle gegen Standard-Referenzagenten zu benchen.

IsaacGymEnvs is a GPU-accelerated physics sandbox and robotics policy training suite designed for reinforcement learning. It serves as a vectorized robotic simulator that runs thousands of parallel environments on GPUs to accelerate the training of neural networks. The project provides a sim-to-real transfer framework that utilizes domain randomization and physics variations to ensure policies trained in simulation are robust enough for deployment on real hardware. It distinguishes itself through a high-performance architecture that uses tensor-based state management to handle observations an

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface