roadmap-marl

name	roadmap-marl
description	Plan multi-agent and simulator-heavy environments using appropriate APIs instead of forcing them into single-agent SB3.
argument-hint	[task-id]
allowed-tools	Read, Write, Glob, Bash
context	repo
agent	three-mlagents-roadmap-marl-planner

Roadmap MARL Skill

Use this skill for multi-agent games, traffic, ecological systems, self-driving, social simulation, LLM agents, or external simulator bridges.

Interface Decision

Choose the interface before implementation:

• Gymnasium: one learning agent controlling one policy.
• PettingZoo AEC: turn-based multi-agent environments.
• PettingZoo ParallelEnv: simultaneous-action multi-agent environments.
• External bridge: simulator owns stepping and exposes a documented protocol.
• Roadmap visualization: frontend demo exists, but training is not yet a scientific baseline.

MARL Requirements

Before marking a task trainable, specify:

• agent ids and lifetimes;
• per-agent observation and action spaces;
• simultaneous versus sequential action semantics;
• reward ownership and global/team rewards;
• termination and truncation per agent;
• wrappers, vectorization, and baseline learner;
• evaluation metrics beyond aggregate reward;
• reproducible seeds and scenario generation.

Baseline Guidance

Use PettingZoo-compatible tooling for true MARL. SuperSuit wrappers may be useful for preprocessing/vectorization. For serious baselines, document whether the plan uses IPPO, MAPPO, RLlib, CleanRL-style scripts, or another maintained open implementation.

Do not quietly convert a MARL problem into a single-agent SB3 task. If a centralized controller is scientifically intentional, name it as such in the task card, UI, and evaluation notes.

Existing Roadmap Tasks

Treat these as roadmap unless and until their backend environment and baseline pipeline are implemented:

• foodcollector
• fish
• intersection
• minecraft
• simcity
• self-driving-car