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 es un framework de aprendizaje por refuerzo y motor de ejecución diseñado para desarrollar y realizar benchmarks de algoritmos de aprendizaje. Proporciona una biblioteca de componentes modulares e implementaciones de referencia utilizadas para construir agentes y establecer líneas base de rendimiento. El sistema permite escalar arquitecturas de agentes desde la ejecución de flujo único hasta grandes entornos distribuidos. Esto facilita la transición desde el prototipado inicial hasta la ejecución distribuida para entrenamiento y evaluación. El framework cubre el desarrollo de aprendizaje por refuerzo y el prototipado de arquitecturas de agentes, proporcionando los bloques de construcción necesarios para comparar nuevos modelos frente a agentes de referencia estándar.

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