Building an AI Model to Play the Whot! Card Game

The article describes the process of building an AI model that can play the Whot! card game using Reinforcement Learning (RL) techniques. The author explains how RL allows the AI agent to learn the game rules and strategies through interaction with the game environment, without the need for labeled training data.

Details

The article introduces the problem of building an AI model that can play the Whot! card game at a human level. The author explains that Whot! is a blend of skill and luck, making it difficult to build a reliable AI model using traditional supervised learning approaches. The solution proposed is to use Reinforcement Learning (RL), a technique that involves exposing an AI agent to the game environment and allowing it to learn by directly interacting with the environment. In RL, the agent learns to take actions that maximize rewards, which are defined by a reward function based on the game rules. The author provides a detailed explanation of how the Whot! AI model was trained using RL, including the concept of state-action value functions and the use of a Q-network to generalize the learning. The model was trained over 50,000 episodes to learn the optimal policy for playing Whot!