RLPolicyOps

RLPolicyOps is a web app (with optional desktop agent) that helps teams deploy reinforcement-learning policies into real systems without breaking things. It provides an evaluation harness, offline RL training pipeline, and a “safety gate” that blocks risky policy updates based on constraints you define (cost, latency, error rate, compliance). You connect logged interaction data (bandit/RL traces) from your product, simulator, or operations workflow, then run standardized counterfactual evaluation, stress tests, and rollback-ready canary deployments. The app focuses on the unglamorous but expensive part of RL: monitoring reward hacking, distribution shift, and silent regressions after deployment. It outputs decision-ready reports for engineers and stakeholders, and keeps an auditable history of policy versions, metrics, and approvals. This is an AI app plus traditional app: the core value is RL evaluation/training, wrapped in practical MLOps workflows.