Reversi42

Tournament-grade Reversi with AI-powered opponents
50-100x faster • 11 AI players • 644 opening sequences • Modern web interface

🚀 Quick Start

pip install reversi42
reversi42

Open http://localhost:8000 in your browser and start playing!

✨ Features

🎯 High-Performance Engine

• Bitboard architecture - 50-100x faster than traditional implementations
• Advanced AI algorithms - Alpha-beta pruning, transposition tables, parallel search
• Configurable depth - From beginner (3 ply) to expert (16 ply)
• Temperature-based variety - Probabilistic move selection for more diverse gameplay

🤖 11 AI Opponents

• ELO ratings: 1250-1880 (beginner to expert)
• Unique strategies: Adaptive, defensive, aggressive, tactical, chaotic
• YAML-based configuration - Create custom AI players without coding
• Move variety - Lower-level players use temperature-based selection for more varied gameplay

📚 Professional Opening Book

• 644 tournament sequences - Derived from professional play
• Trie-based lookup - Instant move suggestions
• Multiple evaluation modes - Balanced and tactical openings

🌐 Modern Web Interface

• Real-time gameplay - FastAPI + WebSocket for instant updates
• Game management - Save/load games in XOT format
• Live analysis - Move evaluation and position visualization

🏆 Tournament System

• Automated competitions - Benchmark AI players
• Statistical analysis - Comprehensive performance metrics
• Custom configurations - Flexible tournament setups

🎮 AI Players

📖 View detailed player profiles →

📦 Installation

From PyPI

pip install reversi42

From Source

git clone https://github.com/lookee/Reversi42.git
cd Reversi42
pip install -e .

Requirements: Python 3.9+

🎨 Custom AI Players

Create your own AI opponents using YAML configuration files—no programming required!

cp config/players/00_AI_CONFIG_TEMPLATE.yaml config/players/enabled/my_ai.yaml
# Edit the configuration file
reversi42  # Your player is automatically discovered!

Move Variety (Temperature): Control how deterministic your AI plays:

• temperature: 0.0 - Always plays the best move (deterministic)
• temperature: 0.1-0.3 - Mostly best moves, occasional variety
• temperature: 0.4-0.5 - High variety for more unpredictable gameplay
• Auto-calculated based on depth (lower depth = higher temperature)

📚 Configuration Guide →

📊 Performance

• Speed: 50-100x faster than array-based implementations
• Search Depth: 4-16 ply (configurable)
• Parallel Processing: Multi-core support
• Memory: Optimized data structures

📖 Documentation

• User Guide - Getting started, rules, strategies
• API Reference - Complete API documentation
• Architecture - System design and technical details
• Tutorials - Create custom AI players
• Tournament Guide - Tournament system usage

🧪 Testing

# Run test suite
./scripts/run_tests.sh

# With coverage
pytest --cov=src tests/

572+ tests covering unit, integration, and characterization scenarios.

Test Coverage by Module

• Apocalyptron AI Engine: 800+ tests (evaluation, ordering, pruning, cache, search, observers)
• Board Module: 34 tests (BoardModel, BoardControl, ViewFactory)
• Players Module: 60+ tests (PlayerFactory, PlayerHuman, PlayerApocalyptron, config system)
• Core Module: 34 tests (config, game_config)
• Infrastructure: 10 tests (persistence/game_io)
• Integration Tests: 50+ tests (board integrity, player isolation, book instant mode)
• Bitboard Tests: Comprehensive move generation tests
• WebGUI Tests: Backend server, WebSocket observer, E2E tests

📈 Project Stats

• ~17,000 lines of Python code
• 572+ tests with comprehensive coverage (target: 80%+)
• 44 test files organized by module
• 40+ documentation files
• 11 AI players pre-configured
• 644 opening sequences from professional play

📄 License

GNU General Public License v3.0 or later
See LICENSE for details.

👤 Author

Luca Amore
🌐 www.lucaamore.com
📧 luca.amore@gmail.com

🙏 Acknowledgments

• Donato Barnaba and Federazione Italiana Gioco Othello (FNGO) - Reversi expertise
• PointyStone3 Project - Opening book contributions

Reversi42 - Professional-grade Reversi for players and researchers
GitHub • PyPI • Issues