README.md

metadata

title: PM-Ops RL Environment
emoji: 📋
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 8000
pinned: false
tags:
  - openenv
  - reinforcement-learning

PM-Ops: Procedural Project-Management RL Environment

A multi-app simulated environment (Ticketing, Codebase, Chat) featuring procedural organizational drift, designed for reinforcement learning training.

What makes it RL-shaped

Every episode samples a fresh OrgConfig -- different label names, priority schemes, team-to-service mappings, and oncall channel names. Frontier LLMs fail because they rely on memorized conventions; a trained agent learns to read the runbook first.

Tasks

Task	Brief	Max Steps
triage	Bug report -- correct label, priority, team, channel	25
incident_routing	Alert -- identify owner via commits, page oncall	25
release_notes	Compile + post release notes in org style	40
dep_update	Coordinate dependency update across all owning teams	40

Action space

The agent calls actions like:

meta.read_runbook -- learn this org's conventions ticketing.create_ticket -- file a ticket with label + priority ticketing.assign_ticket -- assign to correct team chat.post_message -- notify the right oncall channel meta.finish -- end episode, trigger scoring

Reward

Delayed to episode end. Deterministic Python verifier -- no LLM-as-judge. Partial credit for partial correctness.

Sponsor alignment

• AI Labs: direct PM-in-the-loop positioning
• Patronus: schema drift -- env tests whether agents track convention changes
• Scale AI: enterprise workflow simulation
• Meta RFC 004: delayed trajectory reward is the native reward mode