Solving dots & boxes using reinforcement learning

Abstract

Reinforcement learning is being used to solve games which were previously deemed too com- plex to solve, the most notable example in recent years being DeepMind solving Go. Dots and boxes is a 2-person game, known by many names across the world and quite popular with children. Here, a reinforcement learning agent learns to play the game. The goal was to develop an agent which would learn to win games, could intelligently execute complex trapping strategies present in the game, and shed new light on game-playing strategy. A 3x3-sized dots and boxes board was used. The agent learned to defeat a random opponent with a win rate of over 80%, and the next version of the agent learned to defeat the previous agent with a win rate of over 99%. A full game analysis was performed for the agent. Unfortunately, the agent was not intelligent enough to defeat a human player.