alibaba/ROCK

What it solves

ROCK provides a scalable framework for managing sandbox environments used in agentic reinforcement learning (RL). It simplifies the process of building, deploying, and scheduling these environments, ensuring they remain isolated and consistent across different operating systems and hardware configurations.

How it works

ROCK uses a client-server architecture consisting of several core components:

• Admin: Handles the scheduling and deployment of environments as sandboxes.
• Worker: Allocates physical machine resources and executes the sandbox runtimes.
• ROCKlet: A lightweight proxy that manages communication between the SDK and the sandbox.
• SDK/CLI: Tools for developers to build, register, and interact with environments.

It leverages Docker for container orchestration and supports multiple interaction protocols, including GEM, Bash, and Chat, allowing RL agents to interact with the environment through standardized interfaces (e.g., reset and step functions).

Who it’s for

This framework is designed for researchers and developers working on agentic reinforcement learning who need a reliable way to manage large-scale, stateful sandbox environments for training and testing agents.

Highlights

• Massively Scalable: Distributed architecture allows for efficient resource management across multiple nodes.
• Protocol Compatibility: Fully compatible with the GEM protocol for standardized RL environment interfaces.
• Stateful Runtimes: Provides isolated, stateful sandbox environments to ensure security and stability.
• Flexible Deployment: Supports cross-OS image management, such as running Ubuntu images on macOS.
• Unified SDK: Offers a clean Python SDK for seamless interaction with environments and sandboxes.