The-Swarm-Corporation/PARL
PARL (Parallel-Agent Reinforcement Learning) is a training paradigm that teaches models to decompose complex tasks into parallel subtasks and coordinate multiple agents simultaneously.
When you're building sophisticated AI models for complex tasks, this project helps them break down those tasks into smaller, parallel subtasks. It takes your model's outputs and evaluates how well it's leveraging parallel processing and completing subtasks, giving you metrics and rewards that encourage efficient multi-agent coordination. This is for AI researchers and machine learning engineers developing advanced multi-agent systems.
Use this if you are training AI models that need to perform complex tasks faster and more efficiently by coordinating multiple 'sub-agents' concurrently, rather than just doing things step-by-step.
Not ideal if your AI tasks are inherently sequential or simple enough that they don't benefit from decomposition and parallel execution by multiple agents.
Stars
30
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 03, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/The-Swarm-Corporation/PARL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
google-deepmind/concordia
A library for generative social simulation
Mai-xiyu/Minecraft_AI
AI Play Minecraft
mikelma/craftium
A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI...
cocacola-lab/MineLand
Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs
rezaho/MARSYS
Multi-Agent Reasoning Systems