![]() Then, the game environment reacts to the model’s action and rewards or punishes it based on its level of success or failure. The Python environment sets up the game and sends it to SAS Viya to receive the model’s chosen action. This project applies the principles of reinforcement learning to create a model that learns to play Minesweeper using OpenAI Gym and SASrlenv. Each click is a tense gamble between survival or failure! If the player manages to reveal all non-bomb squares, they win the game. In order to predict and avoid those sneaky bombs, all other revealed tiles display a number that represents the quantity of mines inside the square’s eight adjacent tiles. At the beginning of the game, the grid’s squares are unrevealed, and selecting a square reveals its value. The game’s objective is to avoid revealing any mines on its retro two-dimensional grid, which result in an instant loss. Legendary and nostalgic, Minesweeper is simple to play yet difficult to master. Per Stanford: “Reinforcement Learning (RL) is a powerful paradigm for training systems in decision making.” This model impacts the world in many arenas, including in robot automation, natural language processing (NLP), and game AI. Reinforcement learning, a powerful machine learning strategy, specializes in motivating an agent to make the most beneficial decisions in its environment. I implemented an intelligent Minesweeper-playing model using SAS reinforcement learning, and this article covers what it is, how it works, and how you could implement something similar by combining open source software with SAS Viya’s capabilities. Reinforcement learning is an exciting strategy that is versatile and broadly useful in the fields of data science and machine learning. Hi! I’m Daniel, a technical intern at SAS and a student at North Carolina State University.
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