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----
-license: mit
-pretty_name: Chess MCVS - Zone Guided AI
-tags:
-  - chess
-  - game-ai
-  - monte-carlo-tree-search
-  - reinforcement-learning
-  - zone-guidance
-  - adjacency-matrix
-  - hilbert-curve
-  - abc-model
-  - pytorch
-  - numpy
-task_categories:
-  - other
----
-
-# Chess MCVS - Zone Guided AI
-
-**Advanced Monte-Carlo Value Search (MCVS)** engine for the game **Chess** (8x8), powered by a novel **Displacement-based ABC Model** and **Weighted Adjacency Matrices** with **Hilbert-ordered Zone Guidance**.
-
-This repository implements a complete zone-guided reinforcement learning system, including self-play training, neural networks, and comparative tournaments against classic UCT.
-
-## Core Idea
-
-The engine uses:
-- Displacement-based ABC Model with homogeneous coordinates
-- Dynamic Weighted Adjacency Matrices `W = A ⊙ S ⊙ F`
-- Hilbert curve ordering for efficient zone retrieval
-- A learned **Zone Database** that stores winning/losing position patterns
-- **Zone Guidance** (`λ-PUCT`) to bias search toward promising zones
-
-For more information please refer to the paper at: https://doi.org/10.13140/RG.2.2.18795.09764
-
-## Files Overview
-
-| File                        | Purpose |
-|----------------------------|--------|
-| `chess_mcvs.py`     | Main implementation: game logic, ABC model, Zone Database, MCVS, neural networks, incremental training |
-| `mcvs_vs_uct.py`           | 200-game tournament between MCVS and UCT with detailed logging and online learning |
-| `abc_model.py`             | Displacement-based ABC Model |
-| `matrix_model.py`          | Computes the weighted adjacency matrix |
-
-
-## Requirements
-
-## How to use:
-
-A. Incremental Training
-
-python chess_mcvs.py
-
-This runs continuous self-play + training:
-
-1. Generates games using MCVS
-2. Trains the neural networks
-3. Updates and saves the Zone Database
-4. Fully incremental (you can stop and resume anytime)
-
-B. Tournament with Online Learning
-
-This script runs tournament games while the AI learns (NN or just zone guidance).
-
-# With Neural Networks (Full version – Online Learning):
-
-bash: python mcvs_vs_uct.py
-
-What happens:
-
-1. MCVS plays against classic UCT (alternating sides)
-2. Neural Policy and Value networks learn online after every game
-3. Zone Database is updated and saved after each game
-4. Zone guidance turns on automatically after game 1
-5. Creates detailed logs:
-6. chess_full_results.txt — tournament summary
-7. move_log.txt — per-move statistics
-8. learning_log.txt — training progress
-
-# Without Neural Networks (Zone-only Ablation)
-To run the faster ablation version:
-
-1. Open mcvs_vs_uct.py
-2. Change this line near the bottom:
-
-ablation_no_nets=False   # ← Change to True
-
-```bash
-pip install torch numpy
-