Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)

Understand

In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network.

• Deep Q-Learning has shown promising results in games such as Atari and AlphaGo.
• Instead of learning the entire Q-table, it learns an estimate of the Q function that determines a state's policy action.
• We use Q-Learning and deep Q-learning, to learn control policies of four constraint satisfaction games (15-Puzzle, Minesweeper, 2048, and Sudoku).