Challenge: Mastering a deceptively complex board game with reinforcement learning
As an avid board game player in a local gaming association, I usually invest time in understanding rules and developing winning strategies. However, one game consistently defeated me: Quarto. Despite its simple rules, the game's deep complexity made conventional strategic thinking ineffective, leading to repeated losses against a particular friend.
Driven by a mix of competitive spirit and curiosity, I decided to apply cutting-edge reinforcement learning techniques to crack this puzzle. The goal was to create an AI agent capable of discovering optimal Quarto strategies through self-play, similar to DeepMind's AlphaZero approach that conquered chess, shogi, and Go.
Quarto's unique mechanics presented several interesting challenges for reinforcement learning:
- The game alternates between two distinct action types (choosing a piece for your opponent, then placing a piece you've been given)
- Each piece has four binary attributes (tall/short, light/dark, square/circular, hollow/solid), creating complex pattern recognition requirements
- The victory condition requires identifying when four pieces share any common attribute
- The branching factor is significant, with 16 pieces and 16 board positions creating numerous possibilities
