---
license: mit
pipeline_tag: reinforcement-learning
tags:
- chess
- engine
datasets:
- jetbabareal/veri_txt
---

# OXERA: Grandmaster-Style Chess Policy Network

**OXERA** (Optimized Expert-level Engine with Residual Attention) is a high-fidelity chess policy network designed to bridge the gap between engine precision and human intuition. With **11.2 million parameters**, OXERA is trained to replicate the decision-making processes of world-class players, specifically modeled after the gameplay of **Magnus Carlsen** and elite tournament participants (**2500+ ELO**).

## 🚀 Overview

Unlike traditional brute-force chess engines, OXERA operates as a **Positional Intuition Engine**. It does not merely calculate the highest mathematical advantage; instead, it predicts the most likely move a Grandmaster would make in a given position. This results in a highly aesthetic, human-like playing style that prioritizes dynamic piece activity and sophisticated positional understanding.

## 🧠 Model Architecture

- **Base Architecture:** Residual Convolutional Neural Network (128 Filters, 6 Blocks).
- **Input Representation:** 18-plane board encoding (Standard Maia/Lc0 format).
- **Parameters:** 11,280,641.
- **Training Data:** 
    - 700 MB of Lichess data.
    - 250 MB of elite-level Lichess tournament data (Average ELO 2500+).

## 📈 Performance & Fidelity

OXERA excels in **Move Prediction Accuracy**, achieving professional-grade benchmarks in replicating elite human play:
- **Top-5 Accuracy:** **96.3%** (In 96 out of 100 positions, the Grandmaster's choice is within the model's top 5 candidates).
- **Top-1 Accuracy:** ~**46.5%** (Matching the exact move of a world-class player in high-complexity positions).

The model demonstrates a profound understanding of:
*   **Opening Nuances:** High-fidelity replication of modern opening theory.
*   **Strategic Transitions:** Smooth handling of the transition from middle-game to endgame.
*   **Prophylaxis:** A strong tendency to anticipate and neutralize opponent plans before they manifest.

## 🛠️ Implementation & Usage

OXERA is a **Policy-First** network. While it provides exceptional move suggestions based on intuition, it is best utilized alongside a lightweight search algorithm (such as MCTS) to ensure tactical consistency in high-stakes environments.

### Ideal Use Cases:
- **Interactive Analysis:** Studying how a Grandmaster might approach a specific position.
- **Bot Development:** Creating sophisticated chess personalities for platforms like Lichess.
- **Training Tool:** Helping players understand positional concepts rather than just "engine lines."

## 📉 Training Methodology

The model was refined using advanced deep learning techniques to ensure stability and stylistic consistency:
- **Cosine Annealing:** For optimal weight convergence.
- **EMA (Exponential Moving Average):** To provide a balanced, stable version of the network's knowledge.
- **Expert Data Filtering:** Only games from verified high-ELO sources were used to maintain the "Grandmaster" standard.

## ⚠️ License & Usage
This model is intended for research, educational, and analytical purposes. 

---

### Tags:
`Chess AI` `Magnus Carlsen` `Grandmaster Intuition` `Policy Network` `PyTorch` `Leela Chess Zero` `Human-like AI`