hexbot 4.1.2

Python framework for building AI bots for Hexagonal Connect-6

hexbot

A Python framework for building AI bots for Hexagonal Tic-Tac-Toe (Connect-6 on an infinite hex grid). Fast C game engine, multiple neural network architectures, AlphaZero training pipeline, and a live training dashboard.

Home of the Orca bot.

Installation

pip install hexbot

Or from source (for development):

git clone https://github.com/Saiki77/hexbot-building-framework.git
cd hexbot-building-framework
pip install -e .               # core (game engine + torch)
pip install -e '.[dashboard]'  # + dashboard UI
pip install -e '.[all]'        # everything

The C engine auto-compiles on first import (cc/gcc/clang required).

Quick Start

from hexbot import HexGame, Bot, Arena

game = HexGame()
game.place(0, 0)         # P0: 1 stone first turn
game.place(1, 0)         # P1: first of 2
game.place(1, -1)        # P1: second stone

# Any function is a bot
def my_bot(game):
    return game.legal_moves()[0]

result = Arena(my_bot, Bot.heuristic(), num_games=20).play()

Orca Bot

Pre-trained AlphaZero-style bot. The included checkpoint is early-stage (65 iterations) - gets significantly stronger with more training.

from hexbot import Bot
orca = Bot.orca()
move = orca.best_move(game)

Train

# From game collections (fast bootstrap)
python -m orca.scrape --games 5000 --output games.jsonl
python -m orca.sft --data games.jsonl --then-selfplay 50

# Pure self-play
python -m orca.train --iterations 100

# With web dashboard
python train_dashboard.py

Network Architectures

Config	Params	Description
fast	656K	Quick experiments
standard	3.9M	Default Orca (128 filters, 12 ResBlocks)
hex-masked	3.9M	Hex-neighbor masked CNN (recommended for hex)
large	14.5M	Maximum strength (256 filters)
orca-transformer	4.4M	CNN + transformer attention
hex-gnn	432K	Graph neural network on hex topology
multiscale	1.1M	Local CNN + global attention two-tower

python -m orca.train --config hex-gnn --iterations 50

Features

Feature	Description
SFT Pipeline	Train from game collections (JSONL/CSV/text), chain with self-play
Endgame Solver	Deep alpha-beta with transposition cache, integrates with MCTS
Opening Book	Trie-based lookup from winning games, blends with MCTS policy
6 Architectures	CNN, transformer, GNN, multiscale, hybrid, fast
Mixed Precision	FP16 on CUDA for 2x training speed
Distributed	Multi-GPU (DDP) and multi-machine (Ray)
Skill Curriculum	6-level auto-progression: basics through endgame
Ensemble	Average N checkpoints with uncertainty estimation
Model Zoo	Share and download community models
Leaderboard	Rate bots against references, compare head-to-head
Plugin System	Register custom bots and network architectures
Dashboard	REST + WebSocket API, live game replay, charts
30+ API Functions	NN eval, MCTS search, threats, solver, augmentation

Bot Approaches

#	Approach	Complexity
1	Hand-tuned evaluation	Low
2	Evolutionary weights	Low
3	Minimax with alpha-beta	Medium
4	Monte Carlo playouts	Medium
5	Neural network (AlphaZero)	High
6	Hybrid strategies	High

Architecture

hexbot.py              Framework API (Bot, Arena, 30+ analysis functions)
hexgame.py             Game engine (wraps C engine via ctypes)
engine.c               2,300 lines optimized C (bitboard win detection)
bot.py                 Neural networks, MCTS, training pipeline
dashboard.py           Dashboard API (REST + WebSocket)
train_dashboard.py     Training dashboard with web UI
pyproject.toml         pip install config
orca/
  __init__.py          Orca bot loader
  config.py            All tunable parameters
  train.py             Training pipeline (CLI + library)
  sft.py               Supervised fine-tuning from games
  scrape.py            Game downloader
  solver.py            Endgame solver
  openings.py          Opening book
  curriculum.py        Skill-based curriculum
  ensemble.py          Multi-checkpoint ensemble
  distributed.py       Multi-GPU / Ray training
  zoo.py               Model zoo
  leaderboard.py       Bot rating system
  transformer_net.py   Transformer architecture
  hex_gnn.py           Graph neural network
  multiscale_net.py    Multi-scale architecture
  benchmark.py         Platform benchmark (compare Mac/CUDA/CPU)
  checkpoint.pt        Pre-trained weights
docs/                  Detailed documentation
examples/              Example bot scripts

Benchmark

Compare performance across platforms:

python -m orca.benchmark --quick    # ~30 seconds

Measures: C engine speed, NN inference, MCTS search, self-play, training steps, augmentation.

Device Support

Priority	Device	Platform
1	CUDA	NVIDIA GPU (Linux/Windows)
2	MPS	Apple Silicon (macOS)
3	CPU	Any platform

Documentation

Guide	Description
Getting Started	Game API, coordinates, turn structure
Bot Approaches	6 approaches with full code
API Reference	All function signatures
Training Guide	Self-play, SFT, curriculum, distributed
Configuration	Every tunable parameter
Dashboard	REST API, WebSocket
Train Dashboard	Web UI for training
SFT Guide	Train from game collections
Advanced	Solver, opening book, ensemble, MCTS tricks
Orca Bot	Architectures, loading, training

Contributing

Built for the Hexagonal Tic-Tac-Toe community. Contributions welcome.

License

MIT