الميزات الرئيسية لـ novasky-ai/skyrl هي: Reinforcement Learning, Reinforcement Learning Frameworks.
تشمل البدائل مفتوحة المصدر لـ novasky-ai/skyrl: google/dopamine — Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse… huggingface/trl — This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language… alibaba/roll — ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models.… aunum/gold — Reinforcement Learning in Go. inclusionai/areal — AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a… instadeepai/jumanji — 🕹️ A diverse suite of scalable reinforcement learning environments in JAX.
Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter
ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement
This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities. The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficie
