This talk emerged from a year-long journey that began with a simple curiosity: could I teach a computer to play strategy games by itself? It started as a college seminar project, but the topic was chosen purely out of personal interest in reinforcement learning and game AI — this was a hobby from the start. Rather than working with pre-built environments like CartPole or Atari games, the goal was to understand the entire pipeline—from implementing game mechanics to training a neural network that actually learns to win.
The game chosen was Antiyoy, a minimalist turn-based strategy game where players control territories on hexagonal grids, build units and structures, manage resources, and compete for dominance. While the game is simple enough to understand, it presents genuine strategic depth—exactly the kind of challenge that makes reinforcement learning both difficult and rewarding.
The talk walks through the complete development process, focusing not on implementation minutiae but on the fundamental questions and design decisions that anyone building similar systems would encounter. You won't see walls of code or detailed mathematical derivations. Instead, you'll hear about the thinking process, the challenges faced, and the solutions that emerged—all with the goal of demystifying what it actually takes to build a learning agent for complex games.
