rl-experiment-loop

What problem does it solve?

This Skill replaces ad hoc reinforcement-learning experiment coordination with a controlled, task- and study-scoped loop that keeps candidates, metrics, budgets, and launch gates aligned.

Core Features & Use Cases

• Legacy Compatibility: Keeps older prompts working while steering new work toward the newer rlxp-autoloop path.
• Loop Coordination: Organizes audits, candidate proposals, validation, monitoring, result reviews, and report updates across a single experiment lifecycle.
• Safety Guardrails: Reinforces contract approval, budget limits, held-out invariants, and the audited runner boundary so runs are not launched blindly.
• Use Case: Use it when an approved RL study needs a monitored cycle from draft candidate to validated launch, then back through analysis and reporting.

Quick Start

Ask the assistant to run the rl-experiment-loop for your approved task and study so it can coordinate validation, launch gating, monitoring, auditing, and reporting.