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ChessEcon

ChessEcon is a live multi-agent reinforcement learning environment where two LLM agents — Qwen 2.5-0.5B (White, trainable) and Llama 3.2-1B (Black, frozen) — compete at chess for real economic stakes.

Code Demo Cerebral Valley

Core trick

Each game debits entry fees and credits prize pools, and those wallet deltas serve as GRPO rewards for the trainable White agent.

Tags

financeagents

Description

ChessEcon is a live multi-agent reinforcement learning environment where two LLM agents — Qwen 2.5-0.5B (White, trainable) and Llama 3.2-1B (Black, frozen) — compete at chess for real economic stakes. Every game deducts entry fees, awards prize pools, and updates agent wallets, creating a closed economy where strategic play has financial consequences. White trains in real-time using GRPO (Group Relative Policy Optimization) with LoRA adapters, learning from game outcomes as rewards. After 138 training steps, White achieves a 96.4% win rate with wallet growth from 100 to +1,104 units — demonstrating that a 0.5B model can learn dominant chess strategy purely from economic reward signals. The system exposes an OpenEnv 0.1 compliant REST + WebSocket API, making it pluggable by any external agent. A live React dashboard streams games, GRPO metrics, and wallet balances in real-time at hackathon.adaboost.io, running on 4× RTX 3070 GPUs via Cloudflare Tunnel.
dronomyio/hackathon_opendev★ 00 forksTypeScriptCreated Mar 7, 2026 · last push Mar 8, 2026

README excerpt

---
title: ChessEcon
emoji: ♟️
colorFrom: indigo
colorTo: yellow
sdk: docker
app_port: 8000
tags:
  - openenv
  - reinforcement-learning
  - chess
  - multi-agent
  - grpo
  - rl-environment
  - economy
  - two-player
  - game
  - textarena
  - llm-training
license: apache-2.0
---

**Dashboard:** https://chessecon.adaboost.ai

<div align="center">

# ♟️ ChessEcon

### Multi-Agent Chess Economy · OpenEnv 0.1 · GRPO Live Training

[![OpenEnv](https://img.shields.io/badge/OpenEnv-0.1-blueviolet?style=flat-square)](https://github.com/huggingface/openenv)
[![TextArena](https://img.shields.io/badge/TextArena-compatible-orange?style=flat-square)](https://github.com/textarena)
[![License](https://img.shields.io/badge/license-Apache--2.0-green?style=flat-square)](LICENSE)
[![Hackathon](https://img.shields.io/badge/Hackathon-2026-gold?style=flat-square)](https://adaboost.io)

**Live API:** `https://chessecon.adaboost.io`
**Dashboard:** `https://chessecon-ui.adaboost.io`
**Swagger:** `https://chessecon.adaboost.io/docs`
**env_info:** `https://chessecon.adaboost.io/env/env_info`

</div>

---

## Overview

ChessEcon is a **two-player LLM chess environment** where agents compete for economic stakes, fully compliant with the [OpenEnv 0.1](https://github.com/huggingface/openenv) specification.

Two language models play chess head-to-head. Each game costs an entry fee. The winner earns a prize pool. The White agent trains **live** using **GRPO** (Group Relative Policy Optimisation) — every ga

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