Running reinforcement learning (RL) agents in secure sandboxes

Running reinforcement learning (RL) agents in secure sandboxes involves isolating each training episode within its own containerized environment to ensure actions affect only that episode's state, preventing interference with other concurrent rollouts. At production scale, this requires infrastructure capable of managing numerous environments in parallel, rapidly spinning them up and resetting them between episodes, and maintaining strict isolation to minimize latency overhead. Key infrastructure considerations include container lifecycle speed, stateful reset management, resource separation for CPU and GPU tasks, high-concurrency orchestration, and data residency controls. Platforms like Northflank offer solutions by supporting over 100,000 concurrent sandbox environments, ensuring quick environment creation and reset, and utilizing microVM-based isolation technologies such as Kata, Firecracker, and gVisor. They also provide production-ready Bring Your Own Cloud (BYOC) deployment and access through API, CLI, or SSH, addressing the challenges of running RL agents at scale by focusing on fast environment spin-up, clean stateful resets, hard isolation, and support for both ephemeral and persistent environment modes.