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// summary
ROCK is a scalable, client-server framework designed for managing sandbox environments in agentic reinforcement learning. It provides tools for building, scheduling, and deploying environments while ensuring stable operation through various isolation mechanisms. The framework is compatible with GEM-like protocols and offers a unified Python SDK for seamless interaction with sandbox runtimes.
// use cases
01
Standardized environment management for reinforcement learning research and development.
02
Scalable sandbox deployment using a distributed architecture of Admin, Worker, and Rocklet components.
03
Integration with diverse reinforcement learning training frameworks via a unified SDK and multi-protocol support.