Linzwcs/EvoPolicyGym

ENPIRE is an agentic framework that allows AI coding agents to autonomously self-improve real-world robot policies through closed-loop physical trial, error, and evolution

Evolutionary multi-agent runtime that breeds, evaluates, and improves autonomous agents across reproducible epochs to converge on optimization of a goal.

Evaluate and improve models and agents using environments

AgentEvolver: Towards Efficient Self-Evolving Agent System

EvoSkill — An open-source framework that automatically discovers and synthesizes reusable agent skills from failed trajectories to improve coding agent performance.

Resources for our paper: "EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms"

Research infra for creating RL environments, post-training, and evals.

An interface library for RL post training with environments.

An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.